prediction of solar irradiance for bangladesh …bangladesh. matlab’s neural network fitting tool...
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PREDICTION OF SOLAR IRRADIANCE FOR BANGLADESH USING NEURAL
NETWORK
by
Md. Shafiqul Islam
A project report submitted to the Department of Electrical and Electronic Engineering in
partial fulfillment of the requirements for the degree
of
MASTER OF ENGINEERING IN ELECTRICAL AND ELECTRONIC ENGINEERING
Under the supervision of
Dr. Md. Monirul Kabir
Assistant Professor
Department of Electrical and Electronic Engineering
Dhaka University of Engineering and Technology
DHAKA UNIVERSITY OF ENGINEERING AND TECHNOLOGY,
GAZIPUR – 1700, BANGLADESH
April, 2014
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CANDIDATE’S DECLARATION
It is hereby declared that this project report or any part of it has not been submitted
elsewhere for the award of any degree or diploma.
_________________
Md. Shafiqul Islam
Student ID: 092231 (P)
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ACKNOWLEDGEMENT
All praises to Allah, the creator of this universe, who gave us the ability to understand
a bit of His supreme engineering and herewith extract knowledge from nature to improve our
living. This research would not have been possible without His blessing. After that, I would
like to express my profound gratitude to my project supervisor, Dr. Md. Monirul Kabir, for
his continuous support and guidance throughout this study. I would also like to show my
appreciation to my parents and to my wife, who have always been there to support and
inspire me. Moreover, I would like to thank Mr. Partha Kumar Pandit, principal of BATC,
Biman for providing me the official permission to continue my postgraduate study in part-
time basis. He has also helped me to be acquainted with the power and beauty of artificial
neural networks and to be encouraged to carry on my project employing this powerful
artificial intelligent tool. A special thank goes to Mr. Nafis Kabir, who provides me with the
technical and mental support to accomplish this work. Finally, I would like to convey my
regards to all others who are directly or indirectly related to this project by sharing their
ideas, suggestions and supporting me.
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ABSTRACT
In this project a solar irradiation prediction model using Artificial Neural Network
(ANN) and geographical and meteorological training parameters has been developed for
Bangladesh. MATLAB’s neural network fitting tool (nftool) has been used to implement
standard multilayer, feed-forward, and back-propagation neural network. A training data set
has been prepared with the help of the NASA surface meteorology and solar energy database
taking information of 64 different locations of Bangladesh. The input parameters for the
network are: latitude, longitude, elevation, month, average daylight hours, mean earth
temperature and relative humidity while the solar insolations on horizontal surfaces are the
target parameters. Simulation results show good agreement between the estimated and actual
values of insolation. Mean Square Errors (MSE) of training has been found in the range of
0.01267 to 0.00087 for different numbers of neurons in the hidden layer; regression values
are also very much close to 1. The trained neural network model has been tested with ten
numbers of samples which are not used for training to examine its prediction performance.
The lowest and highest error of this proposed model has been found 0.09% and 1.96%
respectively. The comparison of the proposed model with some similar models developed
for Bangladesh and other countries shows that this model has the better solar irradiance
prediction capability even though it is based on very simple design methodology. Due to its
simple design and good prediction performance the proposed model could be used reliably
for predicting insolation of locations where there is no direct irradiance measuring
instruments.
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TABLE OF CONTENTS
ACKNOWLEDGEMENT .................................................................................................... iv
ABSTRACT ............................................................................................................................ v
TABLE OF CONTENTS ...................................................................................................... vi
LIST OF FIGURES ............................................................................................................... ix
LIST OF TABLES ................................................................................................................. xi
NOMENCLATURE ............................................................................................................. xii
Chapter 1 INTRODUCTION .............................................................................................. 1
1.1 Background and Problem Statement ..................................................................... 1
1.2 Literature Review .................................................................................................. 3
1.3 Motivation ........................................................................................................... 13
1.4 Objective of the Project ....................................................................................... 14
1.5 Organization ........................................................................................................ 15
Chapter 2 VARIABILITY AND PREDICTION OF SOLAR RADIATION ............... 16
2.1 Introduction ......................................................................................................... 16
2.2 Solar Radiation Fundamentals: Electromagnetic Spectrum of the Sun .............. 16
2.3 Factors Affecting the Amount of Solar Radiation Received on Earth Surface ... 17
2.3.1 Astronomical Factor ..................................................................................... 17
2.3.2 The Atmospheric Factor ............................................................................... 19
2.4 Other Radiation and Atmospheric Related Parameters ....................................... 19
2.5 Solar Radiation Measurement and Analysis ....................................................... 20
2.5.1 Basic Radiation Measurements .................................................................... 20
vii
2.6 Overview of Solar-power Conversion Technologies .......................................... 22
2.6.1. Photovoltaic ................................................................................................. 22
2.6.2. Concentrating Solar Power ......................................................................... 26
2.7 Solar Power, Solar Irradiance and Insolation ...................................................... 28
2.8 Variability and Predictability of Solar Radiation ................................................ 29
2.9 Solar Irradiance Prediction using Measured Meteorological Parameters ........... 29
2.10 Solar Energy Databases ..................................................................................... 30
Chapter 3 NEURAL NETWORK BASED PREDICTOR ............................................. 32
3.1 Introduction ......................................................................................................... 32
3.2 Theory of Neural Network .................................................................................. 32
3.3 Multilayer Perceptron and its Learning Rules .................................................... 36
Chapter 4 METHODOLOGY ........................................................................................... 41
4.1 Introduction ......................................................................................................... 41
4.2 Neural Network Design Steps ............................................................................. 42
4.2 Data Collection .................................................................................................... 42
4.2.1 NASA Surface Meteorology and Solar Energy Datasets ............................. 43
4.2.2 The Surface Meteorology and Solar Energy (SSE) Website ....................... 44
4.3 Dataset Preparation ............................................................................................. 44
4.4 Neural Network Design ....................................................................................... 47
4.4.1 Neural Network Fitting Tool (nftool) .......................................................... 47
4.5 Neural Network Training .................................................................................... 48
4.6 Results and Discussion ........................................................................................ 49
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4.6.1 Evaluation of the Proposed Model Performance ......................................... 50
4.6.2 Testing the Model with Unknown Input Vectors ......................................... 59
4.6.3 Comparison of the Proposed and Other Similar Models ............................. 61
4.6.4 Comparison of the Model Predicted and Measured Values ......................... 62
4.6.5 Mackey-Glass Time Series .......................................................................... 63
Chapter 5 CONCLUSION AND FUTURE WORK ........................................................ 68
5.1 Conclusion ........................................................................................................... 68
5.2 Future Work ........................................................................................................ 68
APPENDIX I ......................................................................................................................... 69
MATLAB Code ..................................................................................................................... 69
ix
LIST OF FIGURES
Figure No. Title Page No.
Figure 1.1: ANN architecture used incorporating six neurons in a single hidden layer .......... 6
Figure 1.2: ANN architecture used for the estimation of beam solar radiation ....................... 8
Figure 2.1:Commercially available PV systems for producing electricity (a) fixed-tilt PV
arrays; (b) polycrystalline PV modules; (c) fixed-tilt PV arrays; (d) thin-film PV roof
shingles; (e) concentrating PV on 2-axis tracker; (f) building integrated PV. (Courtesy of
NREL Image Gallery, http://images.nrel.gov.) ...................................................................... 24
Figure 2.2: Spectral response functions of selected PV materials illustrating their selective
abilities to convert solar irradiance to electricity. (Courtesy of Chris Gueymard.) ................ 24
Figure 2.3: PV system performance characteristics determined by short-circuit current (I)
and open-circuit voltage (Voc), and maximum power point (P) ............................................ 25
Figure 2.4: PV-array short-circuit current (I) is proportional to solar irradiance incident to
the module. Open-circuit voltage is much less dependent on irradiance level. ...................... 25
Figure 2.5: Combined effects of solar irradiance and array temperature on PV-array power
output. ..................................................................................................................................... 26
Figure 2.6: (a) parabolic trough collector; (b) linear Fresnel collector; (c) dish sterling
engine; (d) power tower and heliostats. (Courtesy of NREL Image Gallery,
http://images.nrel.gov.) ........................................................................................................... 27
Figure 3.1: The basic neuron. ................................................................................................ 34
Figure 3.2: Feed-forward neural network. ............................................................................. 37
Figure 4.1: Steps to develop the ANN based irradiation prediction model ........................... 41
Figure 4.2: Data set preparation work flow ........................................................................... 45
Figure 4.3: Proposed Irradiance Prediction Neural Network Architecture ........................... 47
Figure 4.4: Variation of MSE with the variation of hidden layer neurons ............................ 50
x
Figure No. Title Page No.
Figure 4.5: Variation of Regression (R) with the variation of hidden layer neurons ............ 50
Figure 4.6: Training Performance Curves ............................................................................. 51
Figure 4.7: Network Error Histogram ................................................................................... 52
Figure 4.8: Network Regression Plots ................................................................................... 53
Figure 4.9: Comparison of predicted and actual values for January ..................................... 55
Figure 4.10: Comparison of predicted and actual values for February ................................. 55
Figure 4.11: Comparison of predicted and actual values for March ..................................... 55
Figure 4.12: Comparison of predicted and actual values for April ....................................... 56
Figure 4.13: Comparison of predicted and actual values for May ........................................ 56
Figure 4.14: Comparison of predicted and actual values for June ........................................ 56
Figure 4.15: Comparison of predicted and actual values for July ......................................... 57
Figure 4.16: Comparison of predicted and actual values for August .................................... 57
Figure 4.17: Comparison of predicted and actual values for September ............................... 57
Figure 4.18: Comparison of network outputs and actual values for October ........................ 58
Figure 4.19: Comparison of predicted and actual values for November ............................... 58
Figure 4.20: Comparison of predicted and actual values for December ............................... 58
Figure 4.21: MATLAB simulink model ................................................................................ 59
Figure 4.22: Comparison of measured and predicted values of irradiation for Dhaka city ... 63
Figure 4.23: Mackey-Glass time series ................................................................................. 64
Figure 4.24: Training performance for dynamic input sequences ......................................... 66
Figure 4.25: Regression plots for dynamic input sequences ................................................. 66
Figure 4.26: Actual and predicted irradiance for dynamic input sequences .......................... 67
xi
LIST OF TABLES
Table No. Title Page No.
Table 2-1: List of Mostly Used Solar Radiation Databases ................................................... 31
Table 3-1: Summary of Net Functions ................................................................................... 36
Table 3-2: Neurons Activation Functions .............................................................................. 36
Table 4-1: List of selected locations ...................................................................................... 43
Table 4-2: A Sample of the Full Dataset in MS Excel. ......................................................... 46
Table 4-3: Summary of Trainingfor different number of hidden layer neurons .................... 49
Table 4-4: Summary of the responses of the simulink model to unknown inputs ................. 60
Table 4-5: Comparison of proposed model with other similar models ................................. 61
Table 4-6: Comparison of proposed model with Muztoba Ahmad Khan et. al. model ......... 62
Table 4-7: Comparison of measured and predicted values of irradiation for Dhaka city ...... 63
Table 4-8: Sample dataset incorporating time delay .............................................................. 65
xii
NOMENCLATURE
A : Albedo of the Earth’s Surface
AI : Artificial Intelligence
ANN : Artificial Neural Network
ARD : Automatic Relevance Determination
D : Diffuse Solar Radiation
G : Global Solar Radiation
IH : Direct Solar Radiation on a Horizontal Surface
IN : Direct Solar Radiation at Normal Incidence
K↓ : Total Shortwave Radiation
K↑ : Reflected Solar Radiation
L↑ : Upward Longwave Radiation
L↓ : Downward Longwave Radiation
L* : Net Longwave Radiation
MAPE : Mean Absolute Percentage Error
MBE : Mean Bias Error
MLP : Multilayer Percetron
MSE : Mean Square Error
MRE : Mean Relative Error
NNP Neural Network Solar Irradiation Predictor
p : number of pattern
Q* : Net Radiation
R : Regression
RBF : Radial Basis Function neural network
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RMSE : Root Mean Square Error
SAPV : Stand-alone Photo Voltaic System
SSE : Surface Meteorology and Solar Energy
TMY : Typical Meteorological Year
tp : Desired Output Vector
wi : Synaptic Weights
xi : Network Inputs
yi : Network Output
ϴ : Bias
1
Chapter 1
INTRODUCTION
1.1 Background and Problem Statement
Development of sustainable energy resources has become one of the basic challenges
facing researchers engaged in producing electricity and heat for billions of the earth’s
inhabitants. The energy resources availability has been further irritated by the ever-
increasing the world energy demand. Moreover, current energy production from coal and oil
is damaging the environment. Therefore, it is mandatory to develop the technologies
utilizing renewable and clean energy sources to solve these problems.
The renewable energy resources have shown undeniable benefits with regard to urgent
environmental and political visions which can be considered as the future prospect of energy.
These kinds of energies are expected to share a significant portion of the world primary
energy by next few decades. There are different kinds of renewable energy sources like
geothermal, biofuel, tidal and so on, but wind and solar energies are more available and
accessible than other kinds in this region [1]. Fortunately, Bangladesh is endowed with an
ample solar radiation potential that can be effectively harnessed as renewable energy
resource to mitigate the energy crisis [2].
Recording solar energy data is usually possible through solar measurement equipment
while these devices are not available in some remote or rural locations that specially have
high potential of solar installation. Direct measurements are also not widely available due to
the cost, maintenance and calibration requirements of the measuring equipment [3]. This
limited availability of radiation data motivates the development of computational procedures
to estimate solar radiation from other available meteorological data.
Using estimating tools such as solar radiation prediction models is one of the best
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methods to have a good capability of solar potential investigation. Because of the stochastic
behavior of wind and solar data, accurate data modeling and successful sizing the system are
difficult [4]. In this sense, exact prediction solar and wind data is of vital importance for
energy planning studies [4]. Theoretical and empirical models have been postulated to
compute the components of the solar radiation [4,5]. Some of these models are theoretical,
dealing with the solution of the radiative transfer equation, while others are simply
regression models. Angstrom (1924) presented the first attempt at estimating global solar
radiation was the well-known empirical relation between global solar radiation under clear
sky conditions and bright sunshine duration. These developed empirical models are location
specific and hence are limited in scope and application. For addressing and overcoming these
limitations, nowadays artificial intelligent techniques are being exploited for solar radiation
mapping or modeling in several countries [5].
An artificial neural network (ANN) provides a computationally efficient way of
determining an empirical, possibly nonlinear relationship between a number of inputs and
one or more outputs. ANN has been applied for modeling, identification, optimization,
prediction, forecasting and control of complex systems. Like other fields, a good number of
research works have been found that have utilized ANN for different solar energy
applications [4,5].
In this project work, an ANN based solar irradiance prediction model has been
designed. A simple multilayer feed-forward neural network has been trained with
backpropagation learning rule. Easily available and conventional geographical and
meteorological parameters have been used as the input parameters to predict the insolation
on the horizontal surfaces. For preparing training dataset, National Aeronautics and Space
Administration (NASA) Surface Meteorology and Solar Energy (SSE) database has been
utilized. Training and test performance of the proposed prediction model is satisfactory to
3
consider it as one of the candidate method to predict solar irradiance of any given location of
Bangladesh.
1.2 Literature Review
There are a number of research works related to modeling and prediction of solar
radiation for various solar energy applications. Models that were developed two or three
decades ago are based on analytic formula, numeric simulation, or statistical approaches.
The majority of these models may not be suitable for forecasting purposes because of the
large amount of empirically determined parameters which results in higher prediction errors.
In addition, these models need to acknowledge some behavior of the data. However, these
models cannot be used in the following problems:
Forecasting and modeling the data in long term.
Missing data in the database.
Prediction of the data in the location, where the measurement instruments are not available.
To overcome these limitations, artificial intelligent (AI) techniques are being employed
in most of the recent works. Among the various AI techniques Artificial Neural Network
(ANN) has been employed by the majority of the researchers. ANN based models have been
proved to be superior to the previously done works that were based on conventional
approach. The following section reviews some of the significant ANN based solar irradiance
prediction models that are closely related to proposed model.
1.2.1 Artificial Neural Network techniques for solar radiation prediction
ANN is a section in artificial intelligence (AI) which works as a superb tool for
exploration as it is competent to solve non-linear function estimation, data sorting, pattern
detection, optimization, clustering and simulation. These are called ‘black-box’ modeling
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procedures to carry out non-linear mapping. Its design primarily involves input layer, hidden
layer, output layer, connection weight and biases, activation function and summation node.
Its action is divided into two stages: learning (training) and generalization (recalling). In
training network weights and biases are used to generate the target output by reducing the
error function. The networks are progressed through learning algorithm and trained by
epochs which are entire cycle of all training data existing in the network. The learning
techniques are divided into supervised, unsupervised, reinforcement and evolutionary
learning. The supervised learning is based on totaling of variance between the real network
output and preferred output. The weights and biases are modified by organizing training
pattern set and resultant errors between the preferred output and the subsequent network
output. Thus supervised learning proceeds as closed loop feedback system where error is the
feedback signal. The error degree is characterized through mean squared error(MSE). The
MSE is determined after each epochs and the learning process is finished when MSE is
minimized.
ANN techniques have become alternative methods to conventional techniques and
are used in a number of solar energy applications. Kalogirou [6] has reviewed the use of
ANN in renewable energy systems applications. Mellit et. al. [7] has reviewed ANN for
sizing of photovoltaic systems and Mellit and Kalogirou [5] has reviewed ANN for
photovoltaic applications.
Mohandes et al. [8] used data from 41 collection stations in Saudi Arabia. From
these, the data for 31 stations were used to train a neural network and the data for the other
10 for testing the network. The input values to the network are latitude, longitude, altitude
and sunshine duration. The results for the testing stations obtained are within 16.4% and
indicate the viability of this approach for spatial modeling of solar radiation.
Alawi and Hinai [9] used ANNs to predict global solar radiation in areas not covered
5
by direct measurement. Hontoria et al. [10] improved the generation of hourly solar radiation
artificial series using neural networks. A neural network approach for generating solar
radiation artificial series has been proposed by Zufiria [11].
Mohandes et al. [12] used RBF networks for modeling monthly mean daily values of
global solar radiation on horizontal surfaces and compared its performance with that of a
MLP model and a classical regression model. The proposed network employs as inputs the
latitude, longitude, altitude and sunshine duration. According to the authors, the results on
locations that are not included in the modeling indicate viability of the neural network
methods to solve such problems when compared with a classical regression model. Although
the data sample is relatively small, representing only 1 year from each of 32 locations, it
demonstrates the concept. The average MAPE for the MLP network is 12.6 and the average
MAPE for RBF networks is 10.1.
Hontoria et al. [13] applied a recurrent ANN for modeling the global solar radiation.
The proposed model has been applied and tested in Spanish locations with good accuracy.
Tymvios et al. [14], used an ANN for estimating the total solar energy on a
horizontal surface. Kalogirou et al. [15] used an ANN model for prediction of maximum
solar radiation from relative humidity and temperature. The results obtained indicate that the
correlation coefficient varied between 98.58% and 98.75%.
An ANN-based model for estimation of monthly daily and hourly values of solar
global radiation was proposed by Reddy and Manish [16]. Solar radiation data from 13
stations spread over India have been used for training and testing the ANN. The maximum
mean absolute error between predicted and measured hourly global radiation is 4.07%. The
results indicate that the ANN model show promise for predicting solar global radiation at
places where monitoring stations are not established.
Sozen et al. [17,18] used a neural network for the estimation of solar potential based
6
on geographical and meteorological data (latitude, longitude, altitude, month, mean sunshine
duration and mean temperature) as input of the network. The measured data from 17 stations
in Turkey collected between the years2000 and 2002 were used. One set with data for 11
stations was used for training a neural network and the other data set from six stations was
used for testing. According to the authors, the maximum MAPE was found to be less than
6.7% and R2 values to be about 99.89% for the testing stations. The predictions from the
ANN models could enable scientists to locate and design solar-energy systems in Turkey and
determine the appropriate solar technology. Mellit et al. [19] used the RBF network for
estimating total daily solar radiation data from measured daily sunshine duration. The
correlation coefficient obtained for the validation data set is 97.0%.
Figure 1.1: ANN architecture used incorporating six neurons in a single hidden layer
Sozen et al. [20] proposed an ANN for forecasting mean monthly solar radiation in
Turkey. The proposed model has as input the geographical coordinates, mean sunshine
duration, mean temperature and month. According to the authors, the results indicate that the
ANN model seems promising for evaluating the solar resource potential at the places where
7
there are no monitoring stations in Turkey. Fig. 1.1 shows the proposed ANN for solar
radiation forecasting. According to the authors, the best value of R2 is 99.55% for Siirt
(location in Turkey); similarly, maximum MAPE value is 5.28% for Sakarya (location in
Turkey) and R2 is 99.898% for Artvin. The predicted solar resource values are very close to
the actual values for all the months.
A comparative study of Angstroms and ANN methodologies in estimating global
solar radiation was presented by Tymvios et al. [21], where several models have been
proposed. The parameters used as input were the daily values of measured sunshine duration,
theoretical sunshine duration, maximum temperature and the month number. The period of
data collection was 1986–1992 at Athalassa, Cyprus situated at latitude 35008’ N, longitude
33023’E, altitude 161m. According to the authors, the best ANN model was the one with all
inputs except the month number and the results showed an MBE and RMSE of 0.12% and
0.67%, respectively. The ANN methodology is a promising alternative to the traditional
approach for estimating global solar radiation, especially in cases where radiation
measurements are not readily available.
Lopez et al. [22] proposed the selection of input parameters to model direct solar
irradiance by using ANNs. The Bayesian framework ANN, named as Automatic Relevance
Determination (ARD) Method, was employed to obtain the relative relevance of a large set
of atmospheric and radiometric variables used for estimating hourly direct solar irradiance.
The proposed novel methodology can be used in unfavorable conditions, in terms of limited
amount of available data, giving accurate results.
Alam et al. [23] proposed an ANN model for estimating beam solar radiation. A new
defined parameter, known as Reference Clearness Index (RCI), is introduced. Computation
of monthly mean daily beam solar radiation at normal incidence has been carried out.
According to the authors, the results of ANN model were compared with measured data
8
based on root mean square error (RMSE) and mean bias error (MBE). The RMSE obtained
for the ANN model varied between 1.65% and 2.79% for an Indian region. Fig. 1.2 shows
the proposed ANN used for estimating the beam solar radiation.
Figure 1.2: ANN architecture used for the estimation of beam solar radiation
Elminir et al. [24] proposed an ANN model to predict diffuse fraction in hourly and
daily scale. A comparison between the performances of the ANN model with that of two
linear regression models has been reported. The results show that the ANN model is more
suitable to predict diffuse fraction in hourly and daily scales than the regression models in
the plain areas of Egypt. The predicted values were compared with the actual values and
presented in terms of usual statistics. According to the authors, the ANN model predicted
infrared, ultraviolet and global insolation with a good accuracy of approximately 95%, 93%
and 96%, respectively. In addition, ANN model was tested to predict the same components
for Aswan over an 11-month period. The predicted values of the ANN model compared to
the actual values for Aswan produced an accuracy of 95%, 91% and 92%, respectively. Data
for Aswan were not included as a part of ANN training set. Hence, these results demonstrate
9
the generalization capability of this approach over unseen data and its ability to produce
accurate estimates.
Mubiru and Banda [25] used an ANN for estimating the monthly average global solar
irradiation on the horizontal surface in Uganda. The comparison between the ANN and
empirical method emphasized the superiority of the proposed ANN prediction model.
Estimates obtained for the validation site (Kampala), from the proposed ANN model were
correlated with the measured values giving a correlation coefficient of 0.974. The
corresponding MBE was 0.059 MJ/m2
and the RMSE was 0.385 MJ/m2. These results
indicate an acceptable fitting between the estimated and measured global solar irradiation
values.
Jiang [26] found ANN model better than empirical regression model in predicting
solar radiation. Latitude, altitude and mean sunshine duration are taken as inputs and global
solar radiation as output to predict solar radiation of 13 cities in China. The R2 = 0.97,
RMSE = 1.4 MJ/m2 which show accuracy of ANN model in predicting solar radiation.
Senkal and Kuleli [27] used ANN and physical model to estimate solar radiation for
12 cities in Turkey. The input values to the network are latitude, longitude, altitude, month,
mean diffuse radiation and mean beam radiation. The data of 9 cities are used to train a
neural network and 3 cities to test the network. The RMSE values using the MLP and the
physical model are 54 W/m2 and 64 W/m
2 (training cities); 91 W/m
2 and 125 W/m
2 (testing
cities), respectively. Senkal [28] also used generalized regression neural network (GRNN)
for estimating solar radiation in Turkey. The model uses latitude, longitude, altitude, surface
emissivity, land surface temperature as inputs with solar radiation as output. The results
show that RMSE, R2are 0.1630 MJ/m
2, 95.34% for training stations and for testing stations
as 0.3200 MJ/m2, 93.41% respectively.
An ANN-based forecasting of 24 h ahead solar irradiance is developed by Mellit and
10
Pavan [29] for Trieste in Italy. The MLP consists of one input, hidden and output layer. The
mean daily solar irradiance, the mean daily air temperature and the day of the month (i.e. at
the time t) are given to input layer while the output layer gives 24 h of solar irradiance at the
next day (i.e. at the time t+1). Solar irradiance and air temperature data (from July 1st 2008
to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) spread over Trieste
in Italy are used in training and testing the network. The K-fold cross-validation technique is
used for the validation of MLP-forecaster. The correlation coefficient between predicted and
measured solar irradiance is more than 98% for sunny days and is less than 95% for cloudy
days.
Rahoma et al. [30] developed ANFIS neuro-fuzzy system to predict the solar
radiation in Helwan, Egypt (NARIG) using 10 years (1991–2000) daily solar radiation data.
The result shows that Takagi-Sugeno (TS) fuzzy model provides good accuracy of 96% and
RMSE lower than 6%.
Khatib et al. [31] developed linear, nonlinear, fuzzy logic and ANN models for
estimation of global and diffuse radiation of five sites in Malaysia. The latitude, longitude,
day number and sunshine duration are taken as input parameters. The MAPE of different
models for prediction of global radiation are shown in Table 4-5. The MAPE values for
linear, nonlinear and ANN models for the diffuse radiation are 4.35, 3.74 and 1.53
respectively showing better accuracy of estimation by ANN than other models.
S. Quaiyum et. al. [32] presented an application of artificial neural network to predict
solar radiation from a dataset collected over a span of nine years. Then these forecasted
values are used to size standalone PV systems for different locations of Bangladesh. The
MSE values of different models ranges from 0.0029 to 0.0089.
Wanga et al. [33] used back propagation (BP) neural network for short-term solar
irradiance prediction. The BP neural network with different hidden layer neurons is designed
11
and best one is selected. The network uses solar irradiance data of 24 h and sampling interval
of 1h for prediction. The simulation results show that the double hidden layers contain 18, 13
hidden neurons and R2 is 0.9912.
Hasni et. al. [34] modeled global solar radiation using air temperature, relative
humidity as inputs in south-western region of Algeria. The training is done using LM feed-
forward backpropagation algorithm and transfer function in hidden, output layers is
hyperbolic tangent sigmoid, purelin respectively. The MAPE, R2 are 2.9971%, 99.99%.
Rumbayan et. al. [35] used ANN to estimate the monthly solar irradiation for
Indonesia. The model utilize NASA measured data and 9 inputs variables i.e. average
temperature, average relative humidity, average sunshine duration, average wind speed,
average precipitation, longitude, latitude, latitude and month of the year. The MAPE is found
to be 3.4% with 9 neurons in hidden layer.
Chatterjee and Keyhani [36] used 14 inputs (latitude, ground reflectivity and 12
month irradiance values) to estimate total solar radiation (SR) on tilted surface by ANN.
The output layer contains five neurons corresponding to four quarterly optimum tilt angles
and total solar radiation on tilted surface. The activation function in hidden layer is
hyperbolic tangent and in output layer it is linear. The LM algorithm is used for training. The
number of hidden layers and its neurons are selected randomly. The RMSE becomes small
during training and best validation performance is 3.2033 at epoch 7. The ANN estimates
optimum tilt angle with 30accuracy and can also be used for estimating optimum tilt angles
online.
Yildiz et. al. [37]used two models (ANN-1, ANN-2) for the estimation of solar
radiation in Turkey. The ANN-1 model uses latitude, longitude, altitude; month and
meteorological land surface temperature as inputs whereas ANN-2 model utilizes latitude,
longitude, altitude, month and satellite land surface temperature as inputs. The R2 for ANN-
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1, ANN-2 are 80.41%, 82.37% respectively for testing station showing better estimation of
ANN-2 model than ANN-1 model.
Celik and Muneer [38] used generalized regression neural networks (GRNN) to
predict solar radiation on tilted surface in Iskenderun, Turkey. The GRNN utilizes input
parameters as global solar irradiation on horizontal surface, declination and hour angles. The
R2, MAPE are found to be 98.7%, 14.9 Wh/m
2 respectively.
Artificial neural networks (ANNs) are used by S. A. Kalogirou et. al. [39] for the
performance prediction of large solar systems. The ANN method is used to predict the
expected daily energy output for typical operating conditions, as well as the temperature
level the storage tank can reach by the end of the daily operation cycle. They concluded that
the ANN effectively predicts the daily energy performance of the system; the statistical
R2value obtained for the training and validation data sets was better than 0.95 and 0.96 for
the two performance parameters respectively.
O. Assas et. al. [40] proposed a set of artificial neural network models (ANN) to
estimate daily global solar radiation (GSR) on a horizontal surface using meteorological
variables: (mean daily extraterrestrial solar radiation intensity Go, the maximum possible
sunshine hours So, mean daily relative humidity H, mean daily maximum air temperature T,
mean daily atmospheric pressure P and wind speed Vx) for Djelfa city in Algeria. The results
showed that the two parameters: atmospheric pressure and relative humidity affect the
prediction output of global solar radiation. In addition, the results show that the relative
humidity is the most important features influencing the prediction performance.
M. A. Khan et. al. [41] developed artificial neural network (ANN) model for
estimation of daily global solar radiation on horizontal surface in Dhaka. In this analysis
backpropagation algorithm is applied. Day of the year, daily mean air temperature, relative
humidity and sunshine duration were used as input data, while the daily global solar
13
radiation was the only output of the ANN. The database consists of 1827 daily measured
data, between 2008 and 2012, in term of daily mean air temperature, relative humidity and
sunshine duration and global solar radiation. The data has been collected from Bangladesh
Meteorological Department. Root Mean Square Error (RMSE) and Regression R Value (R),
giving a value of 113.6 Wh/m² and 0.9744, respectively. The results of this study have
shown a better accuracy than other conventional prediction models that have been used up to
now in Bangladesh.
1.3 Motivation
Globally renewable generate 3.47% of total electricity demand; while in Bangladesh, it
is only about 0.45%. The renewable energy policy approved in December 2008 aims at
exploring the country's electricity generating potential from renewable energy resources to
meeting the nagging electricity crisis across the country. The policy encourages the private
and public sectors to develop alternative sources of energy to meet up to 10% of total
electricity demand through renewable energy such as solar, wind, biomass and hydropower
by 2020 [42]. Since Bangladesh is endowed with abundant solar energy resources and the
prevailing weather conditions are also favorable enough, emphasis should be given on
exploring and utilizing solar energy for achieving the renewable energy systems
development goal. Due to the random variation of solar irradiance under changeable weather
conditions, the output power of solar power plant follows the fluctuations of solar irradiance
and this causes great difficulties to balance the power and adjust the frequency of the
regional power systems. Hence, for effective and efficient utilization of solar energy,
Bangladesh needs to develop tools and methodologies for measurement and modeling of
solar radiation. Being a developing country, Bangladesh cannot afford to maintain sufficient
number of solar radiation measurement stations. Therefore, it is rather important to develop
14
methodologies based on inexpensive AI techniques for predicting the solar radiation of
potential locations using meteorological parameters that are easily measured with more
available, economical equipment. In recent years, ANNs have been used for solar radiation
modeling purposes for locations with different geographical and climatic conditions, such as
Saudi Arabia, Oman, Spain, Turkey, China, Egypt, Cyprus, Greece, India, Algeria, the UK
and many others [3–40]. But very few works regarding solar energy prediction using ANN
has been done for Bangladesh [32, 41]. Thus, I have been motivated to develop a solar
irradiance prediction model for Bangladesh applying ANN.
1.4 Objective of the Project
The objectives of this project are:
To prepare a dataset containing geographical and meteorological parameters which
have a good correlation with the irradiation.
To select a neural network structure suitable for prediction task and to train the
developed network with the prepared dataset and observe the training performance
of the network.
To test the trained network with unseen samples for checking the actual response of
the network while it will be put into practice.
To create a simulink model of the best performed network which will take
geographical and meteorological data of a given location as input and provide
insolation on horizontal surfaces as output.
To evaluate its ability in predicting solar power production as it is necessary for the
management of electricity grids, for scheduling of conventional power plants, plant
sizing and also for decision making on the energy market.
15
1.5 Organization
This project report has five chapters. First chapter (Introduction) is the introductory
chapter presenting the background and problem statement, motivations, objectives of the
project work and literature survey.
The second chapter (Variability and Prediction of Solar Radiation) gives an overview
of the solar energy, solar radiation fundamentals and also solar radiation measurement and
modeling techniques. This chapter will also introduce the readers with some solar radiation
databases developed with the aim to be utilized by the solar energy applications.
A brief description of artificial neural network has been presented in the third chapter
(Neural Network Based Predictor). This chapter mainly focuses on multilayer feed-forward
backpropagation neural network.
Chapter four (Methodology) actually describes the design steps that have been
followed to develop the proposed solar irradiance prediction model. This chapter starts with
the description how the training dataset has been prepared using NASA provided database.
Neural network training and test procedures have also been presented elaborately. It also
covers simulation results and their analysis to evaluate the performance of developed
network and to select the best network architecture.
The fifth chapter is the last chapter that has some concluding remarks and suggestions
regarding future work directions based on this effort.
16
Chapter 2
VARIABILITY AND PREDICTION OF SOLAR RADIATION
2.1 Introduction
Knowledge of the quantity and quality of solar energy available at a specific location is
of prime importance for the design of any solar energy systems. Although the solar radiation
is relatively constant outside the earth's atmosphere, local climate influences can cause wide
variations in available insolation on the earth’s surface from site to site. In addition, the
relative motion of the sun with respect to the earth will allow surfaces with different
orientations to intercept different amounts of solar energy.
The main aim of this chapter is to talk about the spatial and temporal variability of
incoming solar irradiance, solar irradiance prediction methods and their importance for
predicting output energy of various solar energy conversion systems. This chapter also deals
briefly with the more difficult problem of how to use other meteorological data to predict
solar radiation data of locations of interest.
2.2 Solar Radiation Fundamentals: Electromagnetic Spectrum of the Sun
The sun emits energy in form of electromagnetic waves which are propagated in space
without any need of a material medium and with a speed, c = 3 x 108 ms-1. Electromagnetic
radiation emitted by the Sun reaching out in waves extends from fractions of an Angstrom to
hundreds of meters, from x – ray to radio waves. An angstrom is a unit of length given by
1A = 10-8
cm = 10-4
μm.
Electromagnetic radiations are usually divided into groups of wavelengths. The
wavelength regions of principal importance to the earth and its atmosphere are the;
Ultraviolet (UV) : (0.3 – 0.4 μm) representing 1.2%
17
Visible (VIS) : (0.4 – 0.74μm) representing 49%
Infrared (IR) : (0.74 – 4.0 μm) representing 49%
It was discovered that 99% of the Sun’s radiant energy to the earth is contained in
these wavelength regions, that is, between 0.3 and 4μm and comes mostly from the
photosphere part of the sun [43].
2.3 Factors Affecting the Amount of Solar Radiation Received on Earth Surface
2.3.1 Astronomical Factor
Only a tiny portion of the energy of the sun reaches the earth’s surface. The sun-earth
distance constitutes one of the factors affecting the amount of solar energy available to the
earth. The earth is known to be orbiting round the sun once in a year and at the same time
rotates about its own axis once in a day. The two motions determine the amount of solar
radiation received on the earth’s surface at any time at any place. The path or the trajectory
of the earth round the Sun is an elliptical orbit with the Sun located at one of the foci of the
ellipse. The implication of this is that the distance of the earth from the sun is variant; hence
the amount of radiation received on the earth surface varies. For example, the shortest
distance of the Sun from the earth is called the perihelion, and is 0.993AU. (Astronomical
unit of distance (AU)=1.496 ×10 km). It takes place on December 21st.
On 4th of April and 5th of October the earth is just at 1AU from the sun, while on 4th
of July, the earth is at its longest distance, 1.017AU from the sun; this position is called
Aphelion. The path of the sun’s rays thus varies with time of the day, season of the year, and
position of the site on the earth’s surface. It becomes shorter towards the noon time, it
decreases towards the perihelion position and increases towards aphelion. Thus the variation
in the sun-earth distance causes variation in the amount of solar radiation reaching the earth
surface. The path of the sun’s ray through the atmosphere is perhaps the most important
18
factor in solar radiation depletion. It determines the amount of radiation loss through
scattering and absorption in the atmosphere.
The eccentricity (E0) of the elliptical orbit is expressed in terms of the sun-earth
distance (r) and the average, this distance over a year. It is given
……………………………………………….(2.1)
where dn is the Julian day number in the year. For example d1=1on January 1 and d365
=365 on December 31.
The elliptical motion of the earth round the sun gives rise to the seasons we experience
on earth, and its rotation about its own axis determines the diurnal variation of the amount of
radiation received. The amount of solar radiation received on a unit horizontal surface area
per unit time at the top of the atmosphere is known as the Extraterrestrial radiation H is
given by
)…………………………………(2.2)
This equation gives the average daily value of extraterrestrial radiation, H0 on a
horizontal surface at the top of the atmosphere, while
)……………………..………………………(2.3)
gives the average hourly value of the extraterrestrial radiation.
where is the latitude of the site,
δ is the declination angle of the sun
is the hour angle
is the sun set hour angle
The corresponding expressions for computing the extraterrestrial radiation on a tilted
surface toward the equator at any latitude in the northern hemisphere are given by
Iqbal(1983). For the daily average, we have
19
…………..…(2.4)
And for the hourly average, we have
…………………………(2.5)
where β is the angle of tilt toward equator
2.3.2 The Atmospheric Factor
The extraterrestrial radiation mentioned above is the maximum solar radiation
available to us at the top of our atmosphere. The variable quantities affecting its amount at
the ground surface are the astronomical factors mentioned above and the atmospheric
factors.
Solar radiation however has to pass through the atmosphere to reach the ground
surface, and since the atmosphere is not void, solar radiation in passing through it is
subjected to various interactions leading to absorption, scattering and reflection of the
radiation. These mechanisms result in depletion and extinction of the radiation, thus reducing
the amount of solar radiation we receive at the ground surface of the earth. Several
atmospheric radiation books describe and discuss these radiation depletion mechanisms.
2.4 Other Radiation and Atmospheric Related Parameters
The knowledge of radiation parameters, such as cloudiness index, clearness index,
turbidity, albedo, transmittance, absorbance and reflectivity of the atmosphere through which
the solar rays pass to the ground surface is very necessary for the utilization of solar energy.
Also the knowledge of the meteorological parameters such as number of sun shine hours
per day, relative humidity, temperature, pressure, wind speed, rainfall etc. is desirable
and important for accurate calculation of parameters of some solar energy devices. For
example it is needed to know the average number of sun shine hours per day for accurate
20
calculation of PV (photovoltaic) power needed in sizing solar power electrification for any
location. In Bangladesh, for example, we have an average of 7 hours of sunshine in a day. In
detailed work, however, this value varies with geographical locations.
The knowledge of the spectral distribution of solar radiation available is also
important for development of semiconductor devices such as photo detectors, light emitting
diodes, power diodes, photo cells, etc; it is also essential in the design of some special solar
energy devices for the direct conversion of solar energy to electricity [43].
2.5 Solar Radiation Measurement and Analysis
It is inevitable to know the potential of solar energy available on daily and monthly
bases at the site for solar energy application, not only in amount but in quality, particularly
its spectral composition. For this, the measurement of solar radiation energy and its spectral
distribution under all atmospheric conditions is undertaken also at many radiation networks
around the world.
Solar radiation energy arriving at the edge of the earth’s atmosphere is carried or
conveyed in electromagnetic spectrum, of wavelengths ranging from about 0.2µm to 4µm, as
said above. These groups of wavelengths of the solar radiation are of principal importance to
the earth and its atmosphere, especially for the calculation of absorption by gases, clouds
and aerosols in the atmosphere and to calculate the spectral variation of the earth-atmosphere
albedo, and also essential for photosynthesis, photobiology and photochemistry in the
atmosphere [43].
2.5.1 Basic Radiation Measurements
The basic radiation fluxes being actively measured and studied in many radiation
network stations globally include the sw-total (global) solar irradiance, sw-direct solar
irradiance, sw-diffuse or sky irradiance. Other radiation fluxes measured are global and
21
diffuse photosynthetic active radiation (PAR), ultraviolet total optical depth and the sun
photometric measurement, and commonly measured radiation parameter is the sun shine
hours. However the brief analysis here on radiation measurements is on the global (total)
solar irradiance, H, direct solar irradiance, Hb, and diffuse sky irradiance, Hd.
2.5.1.1 Global (total) Solar Irradiance
Global solar irradiance, H, which is the total sw-radiation flux, measured on a
horizontal surface on the ground surface of the earth, comprising the direct sw-solar
irradiance, Hb and diffuse sw-sky irradiance, Hd. In simple mathematics, the three fluxes are
connected as in the following
……………………………………………………………………...(2.6)
If all measurements were accurate, wherever two of these fluxes are measured, the
third can easily be obtained, but this is not always so.
Global (total) solar radiation flux is the most easily and commonly measured of all
the radiation fluxes in almost all the radiation network throughout the world. Measurement is
done in the shortwave regions, 0.2 to 4.0µm wavelengths, which includes the photo
synthetically Active Radiation (PAR).
2.5.1.2 Direct solar irradiance, Hb
The direct solar irradiance or solar beam Hb is the component of the total solar
irradiance H, which comes directly from the top of the atmosphere, through the atmosphere,
to the ground surface not deviated, nor scattered nor absorbed. The ratios of it to the total H
i.e. Hb/H and to the extraterrestrial radiation H0, i.e. Hb/H0, are very important atmospheric
radiation parameters in the radiative property of the atmosphere. Hb/H can be used to
indicate the clearness of the atmosphere while Hb/H may be used to indicate the cleanness of
the atmosphere and to determine the transmittance property of the atmosphere.
22
2.5.1.3 Diffuse Sky Irradiance, Hd
This radiation flux is also known as the sky radiation. It is short wave radiation,
coming from the sky covering angular directions of 1800to the sensor. It is incident on the
ground surface as a result of scattering and reflection by particles in the atmosphere. Its ratio
to the total flux H, i.e Hd/H measures the cloudiness and turbidity of the sky and its ratio to
the extraterrestrial radiation H0, i.e. Hd/His expected to measure the scattering co-efficient of
the atmosphere.
2.6 Overview of Solar-power Conversion Technologies
Solar energy can be converted to chemical, electrical, and thermal forms of energy.
This section briefly summarizes the energy-conversion technologies used to generate
electricity, and it introduces the relevant aspects of solar energy prediction.
2.6.1. Photovoltaic
Photovoltaic (PV) systems use semiconductor materials for the direct conversion of
light into electricity by the photoelectric effect, which was first observed by Heinrich Hertz
in 1887 and explained by Albert Einstein in 1905. The amount of electricity produced by the
photoelectric effect is a function of semiconductor composition and the intensity and
wavelength of solar radiation available to the PV device. By 1954, three researchers at Bell
Laboratories had developed the first practical “solar battery” - a PV cell that converted 6%
of the incident solar radiation to electricity [44]. Advances in the research and development
of PV devices have steadily produced increases in conversion efficiency, with the present
world record at 43.5%.
Initially a high-value source of electricity used for space applications with total
production capacities measured in watts, the global PV industry now provides an installed
capacity of more than 40 GW and is growing about 25% annually [45]. PV technologies are
23
used in a variety of collector designs, including flat panels positioned at a fixed tilt or on
Sun-following trackers, integrated into building designs (building-integrated PV, or BIPV)
and deployed in concentrating PV (CPV) systems, as shown in Fig. 2.1. The amount of solar
radiation available to each of these collector modes and orientations requires special
consideration when assessing historical solar resources or when forecasting operational
system performance.
The modular nature of PV systems is well suited to rooftop distributed generation,
where electrical power is produced near the point of use, but is also scalable for larger,
utility-scale central power generation, which requires electricity transmission. Understanding
the spatial variability of solar radiation is important for the success of both distributed- and
central-generation systems. PV systems have a very fast response to changes in solar
radiation (settling time for an individual cell is w10 ms). Therefore, the temporal variations
in solar radiation must be characterized to design and operate a PV system that can provide
the most stable power output.
Photovoltaic devices are based on single- and multicrystalline silicon (most prevalent),
amorphous silicon, microcrystalline silicon, or polycrystalline thin film materials such as
cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS). Multijunction PV
devices have achieved the highest energy-conversion efficiencies. In late 2012, the world
record for PV cell efficiency was 43.5% for a GaInP/GaAs/GaLnNAs(Sb) [46]. To predict
electrical power output, each PV technology requires specific information about the
broadband amount and spectral distribution of solar irradiance available to the device
(Fig.2.2). Because the performance of PV devices depends on several environmental factors,
standards have been developed for rating PV modules based on reference test conditions,
including standards for the spectral distribution of solar irradiance [47].
24
Figure 2.1:Commercially available PV systems for producing electricity (a) fixed-tilt PV
arrays; (b) polycrystalline PV modules; (c) fixed-tilt PV arrays; (d) thin-film PV roof
shingles; (e) concentrating PV on 2-axis tracker; (f) building integrated PV. (Courtesy of
NREL Image Gallery, http://images.nrel.gov.)
Figure 2.2: Spectral response functions of selected PV materials illustrating their selective
abilities to convert solar irradiance to electricity. (Courtesy of Chris Gueymard.)
25
Figure 2.3: PV system performance characteristics determined by short-circuit current (I)
and open-circuit voltage (Voc), and maximum power point (P)
Electrical power is the product of voltage (V) and current (I). The power produced by a
PV device is characterized by an I-V curve. As shown in Fig.2.3, the maximum power point
on an I-V curve is determined by the PV device voltage and current characteristics
corresponding to amount of incident solar irradiance, electrical load, and device temperature.
The short-circuit current varies proportionally with incident solar irradiance (Fig.2.4), and
the power output decreases with increasing device temperature (Fig.2.5).The semiconductor
materials used in a PV device fundamentally determine these response characteristics.
Figure 2.4: PV-array short-circuit current (I) is proportional to solar irradiance incident to
the module. Open-circuit voltage is much less dependent on irradiance level.
26
2.6.2. Concentrating Solar Power
Concentrating solar power (CSP; defined here to exclude CPV) converts solar
radiation to thermal energy to produce steam that powers an electrical generator or to operate
an external combustion engine/generator combination. This utility-scale application relies on
direct (beam) solar radiation, as described below, to generate tens to hundreds of megawatts
of electrical power from a CSP system. There are several methods for concentrating solar
radiation on a thermal receiver to produce working temperatures from 5000 C to more than
10000 C (Fig.2.6). Solar-power towers use hundreds to thousands of heliostats (2-axis Sun-
tracking mirrors) to reflect solar radiation onto a central tower-mounted receiver. The
receiver is an efficient heat exchanger used to transfer solar-thermal energy to a working
fluid, typically a molten salt, stored in large tanks. The heat is used to drive a turbine
generator in a manner similar to that in conventional fossil-fueled power stations.
Figure 2.5: Combined effects of solar irradiance and array temperature on PV-array power output.
Linear trough collector technologies rely on parabolic mirrors or a series of Fresnel
reflectors to concentrate direct solar radiation onto a tubular receiver aligned at the
collector’s line of focus. These modular designs are mounted on 1-axis solar trackers usually
oriented north/south and rotated east to west during the day to continuously focus direct solar
27
radiation onto a linear receiver tube. A heat-transfer fluid circulates through the receiver tube
into a series of heat exchangers where the fluid is used to generate high-pressure superheated
steam before returning to the solar collector. The steam is used by a turbine generator to
make electricity.
Figure 2.6: (a) parabolic trough collector; (b) linear Fresnel collector; (c) dish sterling engine; (d)
power tower and heliostats. (Courtesy of NREL Image Gallery, http://images.nrel.gov.)
Dish Stirling engines are mounted at the focal point of a parabolic-dish reflector that is
continuously aligned with the Sun by a 2-axis tracker. The heat-transfer fluid in the receiver
is heated to 2500 C–700
0 C for use by an external combustion Stirling engine to generate
electrical power. Providing high efficiencies, modular parabolic-dish systems are scalable to
meet the needs of communities for distributed power and those of electrical utilities for
28
central generation. As with all CSP technologies, dish Stirling systems require resource
information for direct (beam) solar irradiance.
2.7 Solar Power, Solar Irradiance and Insolation
Solar irradiance is expressed as a radiant flux density or power density (W/m-2
). The
amount of solar-power available to a conversion system is the solar-irradiance incident to the
collector(s) multiplied by the system’s total effective collector area (W/m-2
× m2 = W).
Insolation is the total amount of energy that has been collected on a surface area within
a given time. While the irradiance denotes the instantaneous rate in which power is delivered
to a surface, the insolation denotes the cumulative sum of all the energy striking the surface
for a specified time interval. This interval must be specified in order to make sense, and the
typical unit of time measurement is the hour. Since energy is equal to the rate of power P
being delivered for a specified time T, the resultant insolation equation is as follows:
Insolation = Power * Time / Area.
Electrical utilities operate their generation systems and bill their customers based on
the amount of energy used or the power during a period of time (kWh). The process of
estimating electrical energy generated by a solar-conversion system is based on the available
solar irradiance and many other factors that address the specific system-design performance
and important environmental factors at the time of interest. PV plants are fairly linear in their
conversion of solar power to electricity; that is, their overall conversion efficiency during
operation typically changes less than 20%. On the other hand, thermal inertia and
thermodynamic nonlinearities make relating CSP production to direct normal irradiance
(DNI)more challenging, at least at short timescales. A number of models are available for
estimating solar-energy conversion system performance [48].
29
2.8 Variability and Predictability of Solar Radiation
Solar radiation varies according to a combination of predictable annual and daily
cycles, and irregular (though not entirely unpredictable) changes in weather. The annual and
daily average variation is predictable within certain bounds; hourly variation over the course
of a day is more difficult to predict. Certain events such as major forest fires and, even more
significantly volcanic eruptions, can produce unexpected declines in solar irradiance for
extended periods of time. Satellite-based forecasting models are currently being developed
and are aimed at reliably providing hourly forecasts on a day-ahead basis. Variability poses a
challenge to large-scale integration of solar resources with the electric grid, but satellite-
based and other forecasting models are currently being developed which can reliably provide
hourly forecasts on a day-ahead basis [49].
2.9 Solar Irradiance Prediction using Measured Meteorological Parameters
It is necessary to have an accurate knowledge of the various components of solar
energy available at the locations of interest for its effective and efficient utilization. These
components of solar energy are sunshine duration, maximum ambient temperature, latitude,
longitude, relative humidity, day of the year, daily clear sky global radiation, total cloud
cover, temperature, clearness index, altitude, months, average temperature, average
cloudiness, average wind velocity, atmospheric pressure, extra-terrestrial radiation,
evaporation, reference clearness index, mean diffuse radiation, mean beam radiation ,soil
temperature. Out of these, global radiation is the most important component of solar
radiation as it gives the total solar availability at a given place. It is measured only at a few
locations because of the high cost involved in the purchase of various equipments and
maintenance thereof. Due to financial constraints, lack of human and technical resources, the
meteorological measurements in general and solar radiation in particular, is limited to few
30
locations. In Bangladesh, there are some sunshine recording stations situated generally in
towns and cities. Most of the developing countries have the similar problem to measure the
actual solar radiation for useful utilization in various solar energy applications. Hence, to
estimate these parameters at locations with no measurements, mathematical, statistical, and
other techniques like neural networks, genetic algorithms, wavelets etc. are being used
globally. Most of these techniques need historical solar radiation data from which necessary
data for a particular location and application can be derived [50-54].
2.10 Solar Energy Databases
Different solar radiation databases have been developed and maintained by different
organizations to provide solar planners and designers, building architects and engineers,
renewable energy analysts, and countless others with extensive solar radiation information.
Table 2.1 shows a list of the solar radiation databases with brief introduction and weblinks.
In this project work, NASA Surface Meteorology and Solar Energy (SSE) database
has been used to prepare the dataset. Freely accessible SSE database is one of the most
powerful and popular solar energy solar energy information providers, used worldwide by
the solar energy professionals. In chapter four, SSE has been briefly described along with the
process of using this database for making the intended datasets.
31
Table 2-1: List of Mostly Used Solar Radiation Databases
Name Features Weblink
National Solar
Radiation
Database
(NSRDB)
The NSRDB contains 45 years (1961-2005) of solar
radiation and supplementary meteorological data from over
1,400 sites in the U.S., plus sites in Guam and Puerto Rico.
http://rredc.nrel.
gov/solar/old_da
ta/nsrdb/
Typical
Meteorological
Year (TMY) Data
Solar and weather data derived from the 1952-1975
SOLMET/ERSATZ database. TMY data are hourly values
of solar radiation and meteorological elements for a 1-year
period. Their intended use is for computer simulations of
solar energy conversion systems and building systems.
Because they represent typical rather than extreme
conditions, they are not suited for modeling extreme or
worst-case conditions.
http://www.nrel.
gov/rredc/solar_
data.html
TMY2 Hourly values of solar radiation and meteorological
elements derived from the 239 locations of the 1961-1990
NSRDB. TMY2 data files are included in the Solar Advisor
Model (SAM).
http://rredc.nrel.
gog/solar/old_da
ta/nsnsr/1961-
1990/tmy2/
TMY3 Hourly values of solar radiation and meteorological
elements derived from the 1961-1990 and 1991-2005
NSRDB. Because they are based on more recent and
accurate data, these new TMY3 data sets are recommended
for use in place of earlier TMY2 data. Can be used in SAM
when saved in EPW format (see guidance in SAM).
http://rredc.nrel.
gov/solar/old_da
ta/nsrdb/1991-
2005/tmy3/
NREL
Measurement &
Instrumentation
Data Center
Nearly real-time measurements from selected stations in
the U.S.
http://www.nrel.
gov/midc/
NREL
Geographic
Information
System (GIS)
Data and maps. http://www.nrel.
gov/gis/solar.ht
ml
NASA Surface
Meteorology and
Solar Energy
Satellite-derived meteorology and solar energy parameters
for 1,195 sites around the world.
http://eosweb.lar
c.nasa.gov/sse/
Solar and Wind
Energy Resource
Assessment
(SWERA)
Source of international DNI maps and data. http://swera.unep
.net
NREL
Concentrating
Solar Power
(CSP) Research
Modeling, analysis, maps. Access to Solar Power
Prospector interactive resource map.
http://www.nrel.
gov/csp/modelin
g_analysis.html
Solar Advisor
Model (SAM)
Simulation model for analyzing and comparing solar power
system costs and performance across a range of solar
technologies and markets.
https://www.nrel
.gov/analysis/sa
m/
NREL Renewable
Resource Data
Center
Clear Sky Irradiance, DNI from Global, Spectral
Irradiances, Solar Position, & PV Watts.
http://www.nrel.
gov/rredc/model
s_tools.html
32
Chapter 3
NEURAL NETWORK BASED PREDICTOR
3.1 Introduction
In this project, Neural Network (NN) has been used as the prediction tool named as
NNP. This chapter is going to provide the underlying theory of NN that might be useful for
the effective utilization of this tool to develop the proposed insolation prediction model. As
multilayer feed-forward back-propagation neural network is considered as the universal
prediction tool, next few sections will be limited to describe this particular type of network.
3.2 Theory of Neural Network
A neural network is a general mathematical computing paradigm that models the
operations of biological neural systems. In 1943, McCulloch, a neurobiologist, and Pitts, a
statistician, published a seminal paper titled ‘‘A logical calculus of ideas imminent in
nervous activity’’ in Bulletin of Mathematical Biophysics [55] and later in Hebb’s famous
Organization of Behavior [56]. The early work in AI was separated between those who
believed that intelligent systems could best be built on computers modeled after brains, and
those like Minsky and Papert [57] who believed that intelligence was fundamentally a
symbol processing of the kind readily modeled on the von Neumann computer. For a variety
of reasons, the symbol-processing approach became the dominant theme in AI in the 1970s.
However, the 1980s showed a rebirth in interest in neural computing.
Hopfield in 1982 [58] provided the mathematical foundation for understanding the
dynamics of an important class of networks. Kohonen in 1984 [59], developed unsupervised
learning networks for feature mapping into regular arrays of neurons. Rumelhart and
McClelland in 1986 [60], introduced the back-propagation learning algorithm for complex,
multilayer networks.
33
Beginning in 1986–1987, many neural networks research programs were initiated. The
list of applications that can be solved by neural networks has expanded from small test size
examples to large practical tasks and large-scale integrated neural network chips have been
fabricated [61]. A neural network is a collection of small individually interconnected
processing units. Information is passed through these units along interconnections. An
incoming connection has two values associated with it, an input value and a weight. The
output of the unit is a function of the summed value. ANNs while implemented on
computers are not programmed to perform specific tasks. Instead, they are trained with
respect to data sets until they learn patterns used as inputs. Once they are trained, new
patterns may be presented to them for prediction or classification. ANNs can automatically
learn to recognize patterns in data from real systems or from physical models, computer
programs, or other sources. An ANN can handle many inputs and produce answers that are
in a form suitable for designers [54]. ANNs can be considered as simplified mathematical
models of brain-like systems and they function as parallel-distributed computing networks.
However, in contrast to conventional computers, which are programmed to perform specific
task, most neural networks must be taught, or trained. They can learn new associations, new
functional dependencies and new patterns. Neural networks obviate the need to use complex
mathematically explicit formulas, computer models and impractical and costly physical
models. Some of the characteristics that support the success of ANNs and distinguish them
from the conventional computational techniques are [54, 62]:
The direct manner in which ANNs acquire information and knowledge about a given
problem domain (learning interesting and possibly nonlinear relationships) through
the ‘‘training’’ phase.
Neural networks can work with numerical or analogue data that would be difficult to
deal with by other means because of the form of the data or because there are so
34
many variables.
Neural network analysis can be conceived of as a ‘‘black box’’ approach and the user
does not require sophisticated mathematical knowledge.
The compact form in which the acquired information and knowledge is stored within
the trained network and the ease with which it can be accessed and used.
Neural network solutions can be robust even in the presence of ‘‘noise’’ in the input
data.
The high degree of accuracy reported when ANNs are used to generalize over a set of
previously unseen data (not used in the ‘‘training’’ process) from the problem
domain.
Figure 3.1: The basic neuron.
All neural network models that have been proposed over the years, share a common
building block, known as a neuron and a networked interconnection structure [63]. The most
widely used neuron model is based on McCulloch and Pitts’ work and is illustrated in Fig.
3.1. According to this figure, the neuron consists of two parts: the net function and the
activation function. The net function determines how the network inputs { :1ix i N } are
combined inside the neuron. In this figure, a weighted linear combination is adopted:
35
1
N
i i
i
z w x
(3.1)
Parameters { :1iw i N } are known as synaptic weights. The quantity is called
the bias (or threshold) and is used to model the threshold. In literature, many other types of
network input combination methods have been proposed. These are summarized in Table 3-
1.The output of the neuron, denoted by in this figure, is related to the network input via
a linear or nonlinear transformation called the activation function; . In various
neural network models, different activation functions have been proposed. The most
commonly used activation functions are summarized in Table 3-2. It lists both the activation
functions as well as their derivatives (provided they exist). In both sigmoid and hyperbolic
tangent activation functions, derivatives can be computed directly from the knowledge of
.
In a neural network, multiple neurons are interconnected to form a network to facilitate
distributed computing. The configuration of the interconnections can be described efficiently
with a directed graph. A directed graph consists of nodes (in the case of a neural network
consists of neurons as well as external inputs) and directed arcs (in the case of a neural
network, synaptic links). Several architectures and algorithms have been developed in
literature [64] for solving different problems. The main ones are described in the following
sub-sections.
36
Table 3-1: Summary of Net Functions
Net functions Formula Comments
Linear
Most commonly used
Higher order (second-order
formula exhibition) 1 1
N N
ij i k
i j
z w x x
yi is a weighted linear
combination of their order
polynomial terms
Delta (Σ, П)
Seldom used for the input
variables. The number of input
terms equals Nd, where d is the
order of the polynomial
Table 3-2: Neurons Activation Functions
Activation
function
Formula Derivatives Comments
Sigmoid
Commonly used,
derivatives can be
computed from f(z)
directly
Hyperbolic
tangent
Inverse
tangent
Less frequently used
Threshold
Gaussian
radial basis
Used for radial basis
network: m and
Linear
3.3 Multilayer Perceptron and its Learning Rules
Multilayer perceptron (feed-forward) networks consist of units arranged in layers with
only forward connections to units in subsequent layers [63]. The connections have weights
associated with them. Each signal traveling along the link is multiplied by a connection
weight. The first layer is the input layer, and the input units distribute the inputs to units in
37
subsequent layers. In subsequent layers, each unit sums its inputs, adds a bias or threshold
term to the sum and nonlinearly transforms the sum to produce an output. This nonlinear
transformation is called the activation function of the unit. The output layer units often have
linear activations. In the remainder of this section, linear output layer activations are
assumed. The layers sandwiched between the input layer and output layer are called hidden
layers and units in hidden layers are called hidden units. Such a network is shown in Fig. 3.2.
Figure 3.2: Feed-forward neural network.
The training data set consists of N training patterns {( , )}, where is the pattern
number. The input vector and desired output vector have dimensions and ,
respectively; is the network output vector for the pattern. The thresholds are handled
by augmenting the input vector with an element and setting it equal to one.
For the hidden unit, the net input and the output activation for the
training pattern are:
(3.2)
38
(3.3)
where denotes the weight connecting the input unit to the hidden unit. For MLP
networks, a typicalactivation function f is the sigmoid, given by
(3.4)
For trigonometric networks, the activations can be thesine and cosine functions. The
output for the training pattern is and is given by
= +
, where (3.5)
where denotes the output weight connecting the input unit to the output unit
and denotes the output weight connecting the hidden unit to the output
unit. The mapping error for the pattern is
)
2 (3.6)
where denotes the element of the desired output vector. In order to train a neural
network in batch mode, the mapping error for the output unit is defined as
)
2 (3.7)
The overall performance of an MLP neural network, measured as mean square error (MSE),
can be written as
(3.8)
The key distinguishing characteristic of the multilayer feed-forward neural networks
(MFNN) with the back-propagation learning algorithm is that it forms a nonlinear mapping
from a set of input stimuli to a set of outputs using features extracted from the input patterns.
The neural network can be designed and trained to accomplish a wide variety of nonlinear
mappings, some of which are very complex. This is because the neural units in the neural
network learn to respond to features found in the input. By applying the set of formulations
of the BP algorithm obtained in the previous sub-section, a calculation procedure of such a
39
learning process is summarized as follows [61]:
Given a finite length input pattern ≤ k ≤ K),
Step 1: Select the total number of layers M, the number of the neurons
in each hidden layer, and an error tolerance parameter
Step 2: Randomly select the initial value of the weight vectors
for and
.
Step 3: Initialization
← ← , and ← .
Step 4: Calculate the neural outputs:
for and
Step 5: Calculate the output error
for .
Step 6: Calculate the output delta’s
.
Step 7: Recursively calculate the propagation errors of the hidden neurons:
=
from the layer M-1, M-2,……to layer 1.
Step 8: Recursively calculate the hidden delta values:
=
.
Step 9: Update weight vector:
+
Step 10: Calculate the error function
Step 11: if then go to step 12; otherwise and go to step 4.
Step 12: if then go to step 13; otherwise go to step 3.
Step 13: Learning is completed. Output the weights.
40
In the procedure listed above, several learning factors such as the initial weights, the learning
rate, the number of the hidden neural layers and the number of neurons in each layer, may be
reselected if the iterative earning process does not converge quickly to the desired point.
Although, the BP learning algorithm provides a method for training MFNNs to accomplish a
specified task in terms of the internal nonlinear mapping representations, it is not free from
problems. Many factors affect the learning performance and must be dealt with in order to
have a successful learning process. Mainly, these factors include the initial parameters,
learning rate, network size and learning database. A good choice of these items may greatly
speed up the learning process to reach the target [65]. Advanced methods for learning and
adaptation in MLPs are presented in [64, 66].
41
Collect Data
Create the Network
Configure the Network
Initialize the Weights and Biases
Train the Network
Validate the Network
Use the Network
Chapter 4
METHODOLOGY
4.1 Introduction
For developing the proposed solar irradiation prediction model for Bangladesh
artificial neural network has been employed. Dataset used for the training of the network has
been made with the help of NASA surface meteorology and solar energy database. Neural
Network Fitting Tool (nftool) of MATLAB is the software tool for designing the network
and other analyses. This chapter is going to describe the significant steps involved in the
development of the model.
Figure 4.1: Steps to develop the NN based irradiation prediction model
42
4.2 Neural Network Design Steps
For using neural networks for any application domain, it needs to follow the standard
steps for designing the network. The work flow (steps) for designing the insolation
prediction network has been shown in the Fig. 4.1. Data collection, while important,
generally occurs outside the MATLAB® environment [67].
4.2 Data Collection
Geographical and meteorological data of 64 locations of Bangladesh for the period of
22 years (1983-2005) were obtained from NASA surface meteorology and solar energy
database[68].64 geographical locations spread all over the country were chosen so that the
dataset could represent all possible variations. Table 4-1 shows the latitude and longitude of
the selected 64 locations.
For predicting insolation on horizontal surfaces in the selected locations, I have
considered 8 different parameters (latitude, longitude, elevation, month, average daylight
hours, minimum and maximum mean earth temperature and relative humidity) as the inputs
of ANN. As these data were gathered for a period of 22 years, the monthly average of
different input data such as maximum and minimum temperature, relative humidity, duration
of sunshine were considered. The single output parameter of the model is insolation on
horizontal surface. For this parameter monthly average of daily insolation has been taken
into account, too.
43
Table 4-1: List of selected locations D
istr
ict
La
titu
de
Lo
ng
itu
de
Dis
tric
t
La
titu
de
Lo
ng
itu
de
Dis
tric
t
La
titu
de
Lo
ng
itu
de
Dis
tric
t
La
titu
de
Lo
ng
itu
de
1 22.095 90.112 17 22.657 92.173 33 25.016 90.010 49 24.590 88.271
2 22.800 90.370 18 23.767 90.383 34 24.247 89.921 50 24.010 89.180
3 22.689 90.641 19 23.500 89.830 35 22.334 89.776 51 24.358 88.639
4 22.641 90.199 20 24.000 90.430 36 23.600 88.700 52 24.450 89.717
5 22.354 90.318 21 23.013 89.822 37 23.183 89.167 53 25.621 88.634
6 22.580 89.970 22 24.920 89.960 38 23.553 89.175 54 25.250 89.500
7 21.744 92.381 23 24.433 90.783 39 22.817 89.550 55 25.750 89.660
8 24.045 91.135 24 23.170 90.100 40 23.900 89.000 56 26.000 89.250
9 23.214 90.636 25 23.850 90.010 41 23.400 89.400 57 25.950 88.950
10 22.267 91.817 26 23.525 90.337 42 23.782 88.616 58 26.271 88.595
11 23.456 91.182 27 24.749 90.403 43 23.130 89.500 59 25.733 89.233
12 21.439 92.008 28 23.617 90.500 44 22.718 89.070 60 26.028 88.459
13 23.016 91.398 29 23.920 90.730 45 24.844 89.376 61 24.422 91.443
14 23.132 91.949 30 24.807 90.829 46 25.100 89.100 62 24.481 91.764
15 22.904 90.829 31 23.715 89.587 47 24.900 88.750 63 25.031 91.404
16 22.830 91.100 32 23.000 90.000 48 24.426 89.018 64 24.892 91.883
4.2.1 NASA Surface Meteorology and Solar Energy Datasets
NASA's Prediction of Worldwide Energy Resource (POWER) Project is developing
data sets from Earth Science Enterprise climate research to support renewable energy
industries. The Surface meteorology and Solar Energy (SSE) Data Set contains solar
parameters principally derived from satellite observations and meteorology parameters from
44
an atmospheric model constrained to satellite and sounding observations. It is a 20-year
climatology (1983-2005) interpolated to a one-degree latitude by one-degree longitude grid.
The global coverage of the SSE data set fills the gap where remote locations lack ground
measurement data. Most ground measurement stations are located near populated regions
that may have natural or urban influence on the local climate. The SSE data set can augment
ground measurement data affected by microclimates. There are parameters for sizing and
pointing solar panels, solar thermal applications, cloud information, temperature, humidity,
and wind parameters. The SSE data are considered accurate for preliminary feasibility
studies of renewable energy projects [68].
4.2.2 The Surface Meteorology and Solar Energy (SSE) Website
The SSE web site <http://eosweb.larc.nasa.gov/sse/> delivers documents on the fly
with a user-friendly interface. All choices are plainly layed out for data retrieval. Users can
access data by entering a particular latitude and longitude location, or panning on an image
of the globe and zooming into the area of interest. Users can create customized data tables by
choosing from an extensive list of over 150 monthly averaged solar energy and meteorology
parameters. Data selection is grouped by their most probable application. Users can select
just the parameter data tables of interest to them. Parameter definitions can be displayed
below their respective data tables. Dynamic data mapping allows users the freedom of
displaying global color maps of monthly averaged parameters or zooming in on any region
as small as six by six degrees of latitude and longitude. Additional resources include
accuracy, methodology, usage statistics and a form for submitting questions [68].
4.3 Dataset Preparation
One of the most important tasks of neural network design is to prepare the dataset for
the network training. In this particular case, the dataset has two parts: Inputs and Outputs.
45
Select locations and their latitude and longitude
Insert the latitudes and longitudes to the SSE website
Select the desired paramenters
Copy these parameters and paste it to MS Excel
Inputs:
1. Latitude
2. Longitude
3. Elevation
4. Month of the Year
5. Monthly Averaged Daylight Hours (hours)
6. Average Minimum Daily Mean Earth Temperature (°C)
7. Average Maximum Daily Mean Earth Temperature (°C)
8. Monthly Average d Relative Humidity (%), and
Targets:
1. Monthly Averaged Insolation Incident on A Horizontal Surface (kWh/m2/day).
Figure 4.2: Data set preparation work flow
46
Meteorological parameters of the dataset have been extracted from the SSE website.
To extract the meteorological parameters of a particular location from the SSE website, we
need to provide the latitude and longitude values of that place. Then the website will prompt
the user to select his desired parameters that will be shown in the next step. Then the
parameters can be copied to MS excel. A graphical representation of the dataset preparation
steps has been provided with the Fig. 4.2.
Table 4-2: A Sample of the Full Dataset in MS Excel.
With the input and target parameters of the 64 locations an MS Excel file has been
created which is then converted into MATLAB compatible file so that it can be directly used
for network training. Table 4-2 shows a sample of the total dataset. First eight of the columns
are the input parameters and the last column is for the target parameters.
47
To data set have made for 64 locations and each locations have data sample for 12
months. So, the total numbers of input-target patterns are 768 (= 64×12).
4.4 Neural Network Design
After the preparation of dataset the next step is to design a suitable neural network
structure and choose a software package to run and analyze the network algorithm. The
network is then trained and simulated to assess the suitability of the network on prediction of
insolation.
Figure 4.3: Proposed Irradiance Prediction Neural Network Architecture
4.4.1 Neural Network Fitting Tool (nftool)
Multi-layer feed-forward back-propagation networks have been designed using the
neural network fitting tool (nftool) of MATLAB.nftool provides a graphical user interface
for designing and training a feedforward neural network for solving approximation (fitting)
problems. The networks created by nftool are characterized by:
One hidden layer (the number of hidden units can be changed by the user)
48
The hidden units have a sigmoidal activation function (tansig or logsig) while the
output units have a linear activation function.
The training algorithm is Backpropagation based on a Levenberg-Marquardt
minimization method (the corresponding Matlab function is trainlm).
The learning process is controlled by a cross-validation technique based on a random
division of the initial set of data in 3 subsets: for training (weights adjustment), for learning
process control (validation) and for evaluation of the quality of approximation (testing). The
quality of the approximation can be evaluated by:
Mean Squared Error (MSE): it expresses the difference between the correct outputs
and those provided by the network; the approximation is better if MSE is smaller
(closer to 0).
Pearson’s Correlation Coefficient (R): it measures the correlation between the
correct outputs and those provided by the network; as R is closer to 1 as the
approximation is better.
4.5 Neural Network Training
For training the networks, the input vectors and target vectors have been randomly
divided into three sets as follows: 70% used for training, 15% used to validate that the
network is generalizing and to stop training before overfitting and remaining 15% used as a
completely independent test set of network generalization.
nftool normally has 10 hidden layer neurons. During training the numbers of hidden
layer neurons have been varied to find the optimum number of neurons where the training
performance is the best. To do that trial and error method has been applied. Number of
neurons has been varied from 5 to 50 and the performance of each network has been
recorded in table 4-3. The next section is going to provide a detail discussion and analysis on
these performance parameters.
49
4.6 Results and Discussion
For assessing the quality of approximation or prediction of the network the MSE and R
values of individual network has been observed. Table 4-3 presents the MSE and Regression
(R) values for training, validation and test corresponding to various number of hidden layer
neurons.. For a network to be a good in prediction its MSE values should be close to zero
and R values should be close to 1. Small MSE and high R values for different models
indicate that this neural network architecture and dataset has the capability to provide us with
the desired insolation prediction model.
Fig. 4.3 shows how the MSE varies with the variation of hidden layer neurons. For 30
neurons i.e. model no. 6 has the lowest MSE of 0.00087 which is very much close to zero.
Fig. 4.4 illustrates the variation of R with the variation of hidden layer neurons. For
each model R value is very much near to 1 with the highest of 0.999019 for model 6.
Therefore, for finalizing the network architecture (i.e. the number of hidden layer
neurons), more detail analysis of the model no. 6 has been done.
Table 4-3: Summary of Training for different number of hidden layer neurons
Mod
el
No.
No.
of
Neu
ron
s MSE R
MAPE Training Validation Test Training Validation Test
1 5 0.01267 0.01737 0.01607 0.98610 0.97776 0.97878 0.0906
2 10 0.00674 0.00530 0.00892 0.99253 0.99422 0.98830 0.063
3 15 0.00141 0.00446 0.00530 0.99840 0.99467 0.99410 0.0354
4 20 0.00199 0.00506 0.00448 0.99771 0.99432 0.99501 0.0387
5 25 0.00189 0.00640 0.00592 0.99790 0.99210 0.99398 0.0396
6 30 0.00087 0.00323 0.00321 0.99902 0.99621 0.99639 0.0269
7 35 0.00127 0.00669 0.00685 0.99857 0.99297 0.99147 0.0361
8 40 0.00250 0.00776 0.00771 0.99726 0.99106 0.99130 0.0467
9 45 0.00123 0.00592 0.00436 0.99861 0.99309 0.99489 0.0324
10 50 0.00201 0.00463 0.00695 0.99768 0.99482 0.99250 0.0389
50
Figure 4.4: Variation of MSE with the variation of hidden layer neurons
Figure 4.5: Variation of Regression (R) with the variation of hidden layer neurons
4.6.1 Evaluation of the Proposed Model Performance
It has been observed that the network with 30 hidden layer neurons has the best
performance. To consider it as the proposed model, some analysis regarding this network
training has been performed. These analyses include: Training performance curve
51
observation, error histogram analysis, regression plot analysis, and comparison of actual
insolation and ANN predicted insolation for different locations.
Next few sections will present the details the network containing 30 hidden layer
neurons. The graphical presentation for training performance curves, error histogram, and
comparison of network predicted values and actual values have been given.
Fig. 4.6 plots the performance training record to check for potential overfitting. This
figure demonstrates that the network performance is good because of the following
considerations:
The final mean-square error is small.
The test set error and the validation set error have similar characteristics.
No significant overfitting has occurred by iteration 42 (where the best validation
performance occurs).
Figure 4.6: Training Performance Curves
There is a small oscillation in the performance curve near epoch 4. After this
oscillation error curves showed the similar tendency of decrease in MSE with increase in
epochs, therefore it can be said that there no problem in network training process.
52
Figure 4.7: Network Error Histogram
Error histogram plot (Fig. 4.7) has also been analyzed for an additional verification of
network performance. The error histogram plot shows the distribution of the network errors.
The blue bars represent training data, the green bars represent validation data, and the red
bars represent testing data. The histogram can give us an indication of outliers, which are
data points where the fit is significantly worse than the majority of data. In this case, it has
been noticed that most of the errors fall around zero line and there are very few points with
an error of greater than zero but within -0.1298 and + 0.1881. So, this error histogram
indicates us that network performance is satisfactory.
In this step regression plots have been generated as shown in Fig. 4.8, which shows the
relationship between the outputs of the network and the targets to validate the network
performance. The four axes represent the training, validation and testing and all data. The
dashed line in each axis represents the perfect result– outputs = targets. The solid line
represents the best fit linear regression line between outputs and targets. The R value is an
indication of the relationship between the outputs and targets. If R=1, this indicates that there
53
is an exact linear relationship between outputs and targets. If R is close to zero, then there is
no linear relationship between outputs and targets. For this case, the training data indicates a
good fit. The validation and test results also show R values that greater than 0.996. So, for
this network, the fit is reasonably good enough for all data sets.
Figure 4.8: Network Regression Plots
Now comparison of actual irradiation and ANN predicted irradiation would be
presented with 12 different plots. Each plot represents actual insolation values and its
corresponding ANN predicted values. There are 12 plots each showing comparison for all 64
locations for a particular month.
Fig. 4.9 to 4.20 are representing the comparison for the month of January to December
respectively. It has been seen that for each month the predicted values are in good agreement
54
with the actual values. There are only few cases where predicted values deviate from the
actual values significantly. It is expected that if this network is deployed for practical use, its
prediction performance will be very much similar to this network.
Considering these analysis results, this network has been finalized as the proposed
model. As a final step a MATLAB Simulink model will be generated. In this Simulink
model all of the weight values and connections have been fixed according to the last
performed training. If this model is provided with an input pattern of 8 parameters, it will
produce an output which represents the insolation in W/m2for the latitude and longitude
given in the input pattern and other meteorological conditions.
55
Figure 4.9: Comparison of predicted and actual values for January
Figure 4.10: Comparison of predicted and actual values for February
Figure 4.11: Comparison of predicted and actual values for March
56
0 10 20 30 40 50 60
5
5.2
5.4
5.6
5.8
6
6.2
6.4
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
Actual
ANNOutput
0 10 20 30 40 50 605.4
5.6
5.8
6
6.2
6.4
6.6
6.8
Locations
Ins
ola
tio
ns
(k
Wh
/m2/d
ay
)
ANNOutput
Actual
0 10 20 30 40 50 603.8
4
4.2
4.4
4.6
4.8
5
5.2
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
Actual
ANNOutput
Figure 4.12: Comparison of predicted and actual values for April
Figure 4.13: Comparison of predicted and actual values for May
Figure 4.14: Comparison of predicted and actual values for June
57
0 10 20 30 40 50 603.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
4.4
4.5
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
ANNOutput
Actual
0 10 20 30 40 50 603.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
4.4
4.5
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
ANNOutput
Actual
0 10 20 30 40 50 603.6
3.7
3.8
3.9
4
4.1
4.2
4.3
4.4
4.5
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
Actual
ANNOutput
Figure 4.15: Comparison of predicted and actual values for July
Figure 4.16: Comparison of predicted and actual values for August
Figure 4.17: Comparison of predicted and actual values for September
58
0 10 20 30 40 50 604.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
Actual
ANNOutput
0 10 20 30 40 50 604.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
Locations
Ins
ola
tio
n (
kW
h/m
2/d
ay
)
Actual
ANNOutput
0 10 20 30 40 50 604
4.05
4.1
4.15
4.2
4.25
4.3
4.35
4.4
4.45
4.5
Locations
Ins
ola
tio
n (
KW
h/m
2/d
ay
)
Actual
ANNOutput
Figure 4.18: Comparison of network outputs and actual values for October
Figure 4.19: Comparison of predicted and actual values for November
Figure 4.20: Comparison of predicted and actual values for December
59
4.6.2 Testing the Model with Unknown Input Vectors
As the final step a MATLAB simulink model of the best trained network has been
generated and tested with the totally unknown input vectors which have not been used during
training. The simulink model as shown in Fig. 4.10 has the option to enter an input pattern
and the model will give an output in response to this input pattern. In this stage, the model
has been tested for 10 numbers unknown in put vectors. Table 4-4 shows the inputs and the
network provided outputs. The deviation between the actual insolation value and the network
predicted value is the indication how accurate this prediction model could be in practical use.
This simulink model generation is the prime objective of the project. This simulink
model can further be used for hardware implementation of the network. Anyone can use this
model or the hardware implementation to predict the insolation on horizontal surface for any
geographical location and for given meteorological condition, provided all the 8 input
parameters are available.
Figure 4.21: MATLAB simulink model for NNP
Moreover, this simulink model can be used as an already built block for any solar
system modeling in MATLAB where irradiation values are required.
If the Input (Input Parameters) button (as shown in Fig. 4.21) is clicked, a new wizard
comes to provide 8 input parameters. After providing all the input parameters, the model is
60
RUN to get the network output. Clicking the Output (Predicted Solar Irradiance) button will
show the output in graphical form.
Table 4-4: Summary of the responses of the simulink model to unknown inputs
From Table 4-4 it has been observed that the highest error is 1.96% while the lowest
error is 0.09%. This percentage error has been computed according to the following
equation:
..……………………(4.1)
Therefore, the test result is showing that the developed insolation prediction model
accuracy is good enough to be used for solar energy application.
The MATLAB programming code has been given in the Appendix I that might be
useful for anyone who wants to work further on it.
Seri
al
no
.
Lati
tud
e
(deg
ree)
Lo
ng
itu
de
(deg
ree)
Ele
vati
on
(m)
Mo
nth
Day l
igh
t
ho
ur
(hr)
Min
. T
em
p.
(0C
)
Max.
Tem
p.
(0C
)
Rela
tive
hu
mid
ity
(%)
Pre
dic
ted
Irra
dia
tio
n
(kW
h/m
2/d
ay)
Act
ual
Irra
dia
tio
n(k
Wh
/m2/d
ay)
Err
or
(%)
1 21.493 92.318 240 01 10.9 10.7 30.9 52.7 4.832 4.80 - 0.67
2 22.248 89.247 11 02 11.4 17.9 39.1 50.8 4.844 4.88 0.74
3 22.908 92.126 345 03 12.0 16.6 36.0 57.2 5.560 5.64 1.42
4 22.693 90.386 50 04 12.7 23 37.6 69.5 5.647 5.76 1.96
5 23.785 89.066 22 05 13.2 24.6 36.3 78.0 5.535 5.53 - 0.09
6 24.227 89.267 59 06 13.6 25.2 33.5 84.4 4.710 4.74 0.63
7 24.267 91.307 225 07 13.4 23.9 31.3 85.8 4.145 4.18 0.84
8 25.799 88.934 194 09 12.3 23.4 31.7 84.8 3.968 3.99 0.55
9 22.198 90.629 30 10 11.6 23.5 32.0 79.4 4.255 4.29 0.82
10 25.016 90.124 307 12 10.6 12.1 28.1 60.8 4.214 4.21 - 0.10
61
4.6.3 Comparison of the Proposed and Other Similar Models
The performance of the developed model needs to be compared with other existing
models that have similar design strategies. The comparison has been made on the basis of
Mean Absolute Percentage Error (MAPE) of the models. Referring to the table 4-5, it has
been observed that the proposed model has the superior performance (MAPE = 2.69%) to
other similar models developed earlier for different countries. Most of these models have
used Multilayer Perceptron (MLP) neural network architecture while few of them have used
Radial Basis Function (RBF) neural network architecture.
Table 4-5: Comparison of proposed model with other similar models
Model NN Type Location MAPE (%) Reference
M Mohandes et. al. MLP Saudi Arabia 12.61 [8]
M Mohandes et. al. RBF Saudi Arabia 10.09 [12]
A. Sozen et. al. MLP Turkey 6.70 [17]
A. Sozen et. al. MLP Turkey 6.78 [18]
A. Azadeh et. al. MLP Iran 6.70 [69]
M. A. Behrang et. al. MLP Iran 5.21 [70]
M. A. Behrang et. al. RBF Iran 5.56 [70]
K. S. Reddy et. al. MLP India 4.07 [16]
Proposed Model MLP Bangladesh 2.69 -
Very few works related to prediction of solar irradiance for Bangladesh using neural
network have been found so far. Salman Quaiyum et. al. [32] has applied neural network for
solar radiation prediction of five big cities of Bangladesh. These predicted solar radiation
values have further been used for PV cell sizing. In this work neural network prediction
performance has been measured on the basis of Mean Squared Error (MSE). The MSE
values ranges from 0.0029 to 0.0087 for the five cities whereas my proposed model has the
MSE of 0.0087.
62
A neural network model has been proposed by Muztoba Ahmad Khan et. al. [41] for
estimating global solar radiation on horizontal surfaces of Bangladesh. In this work neural
network based model has been compared with an empirical model. NN based model has
showed much better performance than that of empirical model. The regression value of my
proposed model is better (higher) than that of Muztoba Ahmad Khan et. al. model. The
Regression values (R) has been presented in the table 4-6 for comparison.
Table 4-6: Comparison of proposed model with Muztoba Ahmad Khan et. al. model
Model Model Type Location R Reference
Muztoba Ahmad Khan et. al. Empirical Bangladesh 0.93040 [41]
Muztoba Ahmad Khan et. al. MLP (NN) Bangladesh 0.97440 [41]
Proposed Model MLP (NN) Bangladesh 0.99902 -
4.6.4 Comparison of the Model Predicted and Measured Values
Finally the predicted values by the proposed model have been compared with the
measured solar irradiation values of Dhaka city for 12 months of the year. These irradiation
data were recorded by the institute of renewable energy of Dhaka University from 1988 to
1998 [70]. It has been observed from the table 4-7 that the predicted and measured values do
not differ too much. Thus the model performed with a reasonable accuracy with the highest
percentage error of 5.08% for the month of May and the lowest is 0.47% for the month of
November.
63
Table 4-7: Comparison of measured and predicted values of irradiation for Dhaka city
Month Measured Predicted Error (%)
January 4.03 4.22 -4.71
February 4.78 4.94 -3.35
March 5.33 5.26 1.31
April 5.71 5.75 -0.70
May 5.71 5.42 5.08
June 4.8 4.59 4.38
July 4.41 4.23 4.08
August 4.82 4.68 2.90
September 4.41 4.27 3.17
October 4.61 4.38 4.99
November 4.27 4.29 -0.47
December 3.92 4.01 -2.30
Figure 4.22: Comparison of measured and predicted values of irradiation for Dhaka city
4.6.5 Mackey-Glass Time Series
The Mackey-Glass series, based on the Mackey-Glass differential equation is widely
regarded as a benchmark for artificial forecasting. This series is a chaotic time series
generated from the following time-delay ordinary differential equation:
64
(4.1)
where, a, b and τ are real numbers. Depending on the values of the parameters, this equation
displays a range of periodic and chaotic dynamics. Fig. 4.23 is the Mackey-Glass time series
for τ=17, a = 0.2 and b = 0.1.
Figure 4.23: Mackey-Glass time series
4.6.5.1 Prediction Results using Time-Varying Input Samples
The solar irradiation prediction model (i.e. NNP) has already been tested with static
input samples and found to be satisfactory. It should also be tested for time varying input
samples to evaluate the dynamic characteristics of the network. According to the Mackey-
Glass time series model, training and testing datasets have been modified incorporating time
delay in the input sequences (shown in Table 4-8). This dataset is different from the previous
one as it has three more input parameters T1, T2 and T3. These three input parameters are
derived from the targets in such a manner to support the time delay principle. The
65
performance analysis results of NNP in response to time varying, dynamic data samples have
been demonstrated with the help of Fig. 4.24 to 4.26. Fig. 4.24 shows that the training
performance is good and no overfitting has been occurred in the training process.
Table 4-8: Sample dataset incorporating time delay
66
Figure 4.24: Training performance for dynamic input sequences
Figure 4.25: Regression plots for dynamic input sequences
The Regression (R) values are also very much closed to 1, therefore the targets and
67
0 10 20 30 40 50 60 70 80 90 1003.5
4
4.5
5
5.5
6
6.5
No. of Samples
Irra
dia
nce
Actual
Predicted
outputs are in good agreement. The regression plots have been shown in Fig. 4.25.
Figure 4.26: Actual and predicted irradiance for dynamic input sequences
The prediction ability of the NNP in response to dynamic inputs has been examined by
comparing the targets and their respective outputs. Fig.4.26 shows a portion of this
comparison. In almost all of the cases the predicted and actual irradiances have very small
difference. These small differences indicate that the NNP is able to predict solar irradiance
with reasonable accuracy.
The following information summarizes the training and testing performance results of
NNP for modified datasets incorporating delays in the input sequence.
MSE = 0.000705144
R = 0.99864
MAPE = 2.227%
Therefore, it can be said that NNP has the capability to be trained with dynamic input
samples and provide outputs accordingly. If this network is provided with a set of sample
input sequences, it would predict the values likely to appear in future.
68
Chapter 5
CONCLUSION AND FUTURE WORK
5.1 Conclusion
The use of neural networks in predicting irradiation in horizontal surfaces in
Bangladesh has been investigated in this work. It has been found that the NN based
prediction models (NNP) can predict solar radiation data accurately using easily available
meteorological and geographical parameters. Comparing the proposed model (NNP) with
other available models in different criteria it has been found to be better and promising.
Moreover, this NNP has well response for both static and dynamic inputs. The use of this
technique in the remote locations of Bangladesh where solar measurement devices are not
available can be beneficial as an effective tool to select the most efficient locations for
exploiting solar energy, and to get an idea about the output power of potential solar energy
system that may be useful for energy system planning, design and operation.
5.2 Future Work
Here, nftool of MATLAB has been employed which has few limitations. One of the
limitations of this model is to find the optimal number of hidden layer neurons by trial and
error method, it is not automatically determined. Another problem may be encounter; during
training this network may become stagnant to a local minimum instead of the global. Hence,
further extensive works might be needed to address these issues and make the model better
and competitive. Precise actual data needs to be recorded and compared with the model for
validating the model acceptance. This type of prediction problem having only one output
parameter can also be implemented by Support Vector Machine (SVM) and other state of the
art tools. Hence, it is still to reveal which technique will serve the best until a comparative
study of the fully developed and mature models is done.
69
APPENDIX I
MATLAB Code
% Solve an Input-Output Fitting problem with a Neural Network
% Script generated by NFTOOL
% Created Sun 22 10:31:33 BDT 2013
%
% This script assumes these variables are defined:
%
% FinalIN - input data.
% FinalTG - target data.
inputs = FinalIN';
targets = FinalTG';
% Create a Fitting Network
hiddenLayerSize = 30;
net = fitnet(hiddenLayerSize);
% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainRatio = 60/100;
net.divideParam.valRatio = 20/100;
net.divideParam.testRatio = 20/100;
% For help on training function 'trainlm' type: help trainlm
% For a list of all training functions type: help nntrain
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse'; % Mean squared error
% Choose Plot Functions
70
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
% Train the Network
[net,tr] = train(net,inputs,targets);
% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
% Recalculate Training, Validation and Test Performance
trainTargets = targets .* tr.trainMask{1};
valTargets = targets .* tr.valMask{1};
testTargets = targets .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotfit(net,inputs,targets)
%figure, plotregression(targets,outputs)
%figure, ploterrhist(errors)
71
Bibliography
[1] I. Dincer; “Renewable energy and sustainable development: a crucial review”;
Renewable and Sustainable Energy Reviews; Vol. 4(2), PP. 157–175; 2000.
[2] M. Hussain; “Bangladesh energy resources and renewable energy prospects”; Energy,
Vol. 12(5), PP. 369-374, 1985.
[3] V. Badescu; “Modeling Solar Radiation at the Earth’s Surface Recent Advances”;
Springer Verlag, 2008.
[4] S. Kalogirou and A. Sencan; “Artificial Intelligence Techniques in Solar Energy
Applications, Solar Collectors and Panels, Theory and Applications”; Dr. Reccab
Manyala (Ed.), ISBN: 978-953-307-142-8, InTech, 2010.
[5] A. Mellit and S.A. Kalogirou; “Artificial intelligence techniques for photovoltaic
applications: A review”; Progress in Energy and Combustion Science 34, pp. 574-632
Elsevier, 2008.
[6] S. A. Kalogirou; “Artificial neural networks in renewable energy systems applications: a
review”; Renewable and Sustainable Energy Review, Vol. 5(4), PP. 373–401, 2001.
[7] A. Mellit, S. A. Kalogirou, L. Hontoria and S. Shaari; “Artificial intelligence techniques
for sizing photovoltaic systems: a review”; Renewable and Sustainable Energy Reviews,
Vol. 13, PP. 406–19, 2009.
[8] M. Mohandes, S. Rehman, and T.O. Halawani;“Estimation of global solar radiation
using artificial neural networks”; Renew Energy, Vol. 14(1-4), pp. 179-184,1998.
[9] S. M. Alawi, and H.A. Hinai; “An ANN-based approach for predicting global radiation
in locations with no direct measurement instrumentation”; Renew Energy, Vol. 14(1-4),
1998.
[10] L. Hontoria, J. Riesco, and P. Zufiria; “Aguilera J. Improved generation of hourly solar
radiation artificial series using neural networks”; EANN99, Warsaw; 1999.
[11] P. Zufiria, A.V. Zquez, J. Riesco, J. Aguilera, L. Hontoria; “A neural network approach
for generating solar radiation artificialseries”; Proc. of the IWANN099, Alicante, Spain,
1999.
[12] M. Mohandes, A. Balghonaim, M. Kassas, S. Rehman, and T. O. Halawani; “Use of
radial basis functions for estimating monthly mean daily solar radiation”; Sol Energy,
Vol. 68(2), PP. 161–168, 2000.
[13] L. Hontoria, J. Riesco, P. Zufiria, and J. Aguilera; “Application of neural networks in the
solar radiation field. Obtainment of solar radiation maps”; 16th
European photovoltaic for
chemical engineers, vol. Amsterdam, pp. 385–408, Elsevier, 2000.
[14] F. Tymvios, C. P. Jacovides, and S. C. Michaelides; “The total solar energy on a
horizontal level with the use of artificial neural networks”; 6th
Hellenic conf. of
meteorology, climatology and atmospheric physics, Ioannina, Greece, 2002.
[15] S. Kalogirou, S. C. Michaelides, and F. S. Tymvios; “Prediction of maximum solar
radiation using artificial neural networks”; World Renewable Energy Congress VII
(WREC 2002), 2002.
72
[16] K. S. Reddy, and R. Manish; “Solar resource estimation using artificial neural networks
and comparison with other correlation models”; Energy Convers Manage., Vol. 44, PP.
2519-2530, 2003.
[17] A. Sozen, E. Arcaklyogelu, M. Ozalp, and E. G. Kany´t; “Use of artificial neural
networks for mapping the solar potential in Turkey”; Appl. Energy, Vol. 77, PP. 273-
286, 2004.
[18] A. Sozen, E. Arcakly´ogelu, and M. Ozalp; “Estimation of solar potential in Turkey by
artificial neural networks using meteorological and geographical data”; Energy Convers
Management, Vol. 45(18-19), PP. 3033-3052, 2004.
[19] A. Mellit, M. Benghanem, A. Hadj, and A. Guessoum; “Modeling of global solar
radiation data from sunshine duration and temperature using the Radial Basis Function
networks”; The IASTED, MIC, February 22–25, 2004, Grindelwald, Switzerland, 2004.
[20] A. Sozen, E. Arcakly´ogelub, M. Ozalpa, and N.C. Agclarc; “Forecasting based on
neural network approach of solar potential in Turkey”; Renew. Energy, Vol. 30, PP.
1075-1090, 2005.
[21] F.S. Tymvios, C.P. Jacovides, S.C. Michaelides, and C. Scouteli; “Comparative study of
Angstroms and artificial neural networks methodologies in estimating global solar
radiation”; Sol. Energy, Vol. 78, PP. 752-762, 2005.
[22] G. Lopez G, F. J. Batlles, and J. Tovar-Pescador; “Selection of input parameters to
model direct solar irradiance by using artificial neural networks”; Energy, Vol. 30, PP.
1675-1684, 2005.
[23] S. Alam, S. C. Kaushik, and S. N. Garg; “Computation of beam solar radiation at normal
incidence using artificial neural network”; Renew. Energy, Vol. 31, PP. 1483-1491,
2006.
[24] H. K. Elminir, Y. A. Azzam, and F.I. Younes; “Prediction of hourly and daily diffuse
fraction using neural network, as compared to linear regression models”; Energy, Vol.
32, PP. 1513-1523, 2007.
[25] E.J. Mubiru, andK. B. Banda;“Estimation of monthly average daily global solar
irradiation using artificial neural networks”; Sol. Energy 2007.
[26] Y. Jiang; “Computation of monthly mean daily global solar radiation in China using
artificial neural networks and comparison with other empirical models”; Energy, Vol. 34,
PP. 1276–83, 2009.
[27] O. Senkal and T. Kuleli; “Estimation of solar radiation over Turkey using artificial
neural network and satellite data”; Applied Energy, Vol. 86, PP. 1222–8, 2009.
[28] O. Senkal; “Modeling of solar radiation using remote sensing and artificial neural
network in Turkey”; Energy, Vol. 35, PP. 4795–801, 2010.
[29] A. Mellit and A. M. Pavan; “A 24-h forecast of solar irradiance using artificial neural
network: application forperformance prediction of a grid-connected PV plant at Trieste,
Italy. Solar Energy, Vol.84, PP. 807–21, 2010.
[30] W. A. Rahoma, U. A. Rahoma and A. H. Hassan; “Application of neuro-fuzzy
techniques for solar radiation”; Journal of Computer Science Vol. 7(10), PP. 1605–11,
2011.
73
[31] T. Khatib, A. Mohamed, M. Mahmoud and K. Sopian;“Modeling of daily solar energy
on a horizontal surface for five main sites in Malaysia”; International Journal of Green
Energy, Vol.8, PP. 795–819, 2011.
[32] S. Quaiyum, S. Rahman and S. Rahman; “Application of Artificial Neural Network in
Forecasting Solar Irradiance and Sizing of Photovoltaic Cell for Standalone Systems in
Bangladesh”; Int. Journal of Comp. App. Vol. 32(10), pp. 51-56, 2011.
[33] Z. Wanga, F. Wanga and S. Sub; "Solar irradiance short-term prediction model based on
BP neural network”; Energy Procedia, Vol. 12, PP. 488–94, 2011.
[34] A. Hasni, A. Sehli, B. Draoui, A. Bassou and B. Amieur; "Estimating global solar
radiation using artificial neural network and climate data in the south- western region of
Algeria”; Energy Procedia,Vol. 18, PP. 531–7, 2012.
[35] M. Rumbayan, A. Abudureyimu and K. Nagasaka;“Mapping of solar energy potential in
Indonesia using artificial neural network and geographical information system”;
Renewable and Sustainable Energy Reviews, Vol.16, PP. 1437–49, 2012.
[36] A. Chatterjee and A Keyhani;“Neural network estimation of microgrid maximum solar
power”; IEEE Transactions on Smart Grid, Vol. 3(4), PP. 1860–6, 2012.
[37] B. Y. Yildiz, M. Şahin, O. Şenkal, V. Pestemalci and N. A. Emrahoğlu; “Comparison of
two solar radiation models using artificial neural networks and Remote sensing in
Turkey”; Energy Sources, Part A,Vol. 35, PP. 209–17, 2013.
[38] A. N. Celik and T. Muneer; “Neural network based method for conversion of solar
radiation data”; Energy Conversion and Management,Vol. 67, PP. 117–24, 2013.
[39] S.A. Kalogirou, E. Mathioulakis and V. Belessiotis; "Artificial neural networks for the
performance prediction of large solar systems"; Renewable Energy, Vol. 63, PP. 90-97,
2014.
[40] O. Assasa, H. Bouzgoub, S. O. Fetaha, M. Salmia and A. Boursasa; "Use of the Artificial
Neural Network and Meteorological Data for Predicting Daily Global Solar Radiation in
Djelfa, Algeria"; Int. conf. on Comp. Mat. & Ren. Energy Appl. (ICCMREA), PP. 1-5,
IEEE, 2014.
[41] M. A. Khan, S. Huque and A. Mohammad; “A Neural Network Model for Estimating
Global Solar Radiation on Horizontal Surface”; Proceedings of Int. Conf. EICT-2013,
KUET, Khulna, Bangladesh, PP. 1-4, IEEE Xplore, 2014.
[42] "Interim Action Plan for Improvement of Energy Efficiency & Conservation (2012-
2016)"; Power Division, Ministry of Power, Energy & Mineral Resources, Government
of the People’s Republic of Bangladesh, 14 October, 2012; availlable at:
http://www.powerdivision.gov.bd/powerdivision/uploads/32.pdf.
[43] E. B. Babatunde;“Solar Radiation”; InTech, Croatia, 2012, ISBN 978-953-51-0384-4.
[44] J. Perlin; “Silicon Solar-Cell Turns 50”;. NREL Report No. BR-520–33947, 2004.
[45] “Renewables 2011 Global Status Report, Renewable Energy Policy Network for the 21st
Century”; Paris:REN21 Secretariat.
www.ren21.net/Portals/97/documents/GSR/REN21_GSR2011.pdf
[46] S. Kurt; “Opportunities and Challenges for Development of a Mature Concentrating
Photovoltaic Power Industry”; NREL Technical Report. NREL/TP-5200–43208, 2012.
74
[47] ASTM Standard G173-03; “Standard Tables for Reference Solar-Spectral Irradiances:
Direct Normal and Hemispherical on 370Tilted Surface”; ASTM International, West
Conshohocken, PA, 2008. http://dx.doi.org/10.1520/G0173-03R08. www.astm.org.
[48] B. Marion, M. Anderberg, and P. Gray-Hann; “Recent Upgrades and Revisions to
PVWATTS”; In: Campbell-Howe, R. (Ed.), Proceedings of the Solar-2006 Conference,
Denver, 2006.
[49] E. Aguado; “Local-scale variability of daily solar radiation—San Diego County,
California”; Journal of Climate & Applied Meteorology, vol.25, no.5, pp.672–678, 1986.
[50] T. Muneer, S. Younes, and S. Munawwar; “Discourses on solar radiation modeling”;
Renewable and Sustainable Energy Reviews, vol.11, no.4, pp.551–602, 2007.
[51] M. Donatelli, G. Bellocchi, and F. Fontana; “RadEst3.00: software to estimate daily
radiation data from commonly available meteorological variables”; European Journal of
Agronomy, vol.18, no. 3-4, pp. 363–367, 2003.
[52] S. Safi, A. Zeroual, and M. Hassani; “Prediction of global daily solar radiation using
higher order statistics”; Renewable Energy, vol.27, no.4, pp.647–666, 2002.
[53] S. Kalogirou and A. Sencan; “Artificial Intelligence Techniques in Solar Energy
Applications, Solar Collectors and Panels, Theory and Applications”; Dr. R. Manyala
(Ed.), ISBN: 978-953-307-142-8, InTech, 2010.
[54] S. A. Kalogirou; "Artificial intelligence for the modeling and control of combustion
processes: a review"; Prog Energy Combust Sci, vol. 29, pp.515–66, 2003.
[55] W. S. McCulloch and W. A. Pitts;“A logical calculus of the ideas imminent in nervous
activity”; Bull Math Biophys, vo1. 35, pp. 115–133, 1943.
[56] D. O. Hebb;“The organization of behavior”; New York: Wiley, 1949.
[57] M. Minsky, S. Papert; “Perceptrons”; Cambridge, MA: MIT Press; 1969.
[58] J. J. Hopfield; “Neural networks and to physical systems with emergent collective
computational abilities”; Proc Natl Acad Sci, Vol. 79, pp. 2554–2558, 1982.
[59] T. Kohonen; “Self-organization and associative memory”; New York: Springer; 1984.
[60] D. E. Rumelhart, J. L. McClelland;“The PDP research group, parallel distributed
processing: explorations in the microstructure of cognition”; Cambridge, MA: MIT
Press/Bradford Books; 1986.
[61] F. Robert;“Neural fuzzy systems”; Abo Akademi University, 1995.
[62] J. Nannariello, F. R. Frike; “Introduction to neural network analysis and its applications to
building services engineering”; Build Serv Eng Res Technol, Vol.22(1), pp.58–68, 2001.
[63] H. H. Yu andH. Jenq-Neng; “Handbook of neural network signal processing”; Boca
Raton, FL: CRC press, 2001.
[64] S. Haykin; “Neural networks: a comprehensive foundation”; Second ed. New York:
Macmillan, 1999.
[65] M. M. Gupta, J. Liang, and H. Noriyasu; “Static and dynamic neural networks, from
fundamentals to advanced theory”; IEEE press, Wiley; 2003 [Forward by Lotfi A. Zadeh.].
[66] C. J. Lakhmi and N. M. Martin;“Fusion of neural networks, fuzzy systems and genetic
algorithms: industrial applications”; Boca Raton, FL: CRC Press LLC, 1998.
[67] M. H. Beale, M. T. Hagan, and H. B. Demuth; “Neural Network ToolboxTM for use
with MATLAB®, User’s Guide”; Mathworks Inc., ver.8.0.1, 2013.
75
[68] NASA Surface meteorology and Solar Energy database websit. Available:
https://eosweb.larc.nasa.gov/cgi-bin/sse/
[69] A. Azadeh, A. Maghsoudi and S.Sohrabkhani; “An integrated artificial neural networks
approach for predicting global radiation”; Energy Conversion and Management,
Elsevier, Vol. 50(6), pp. 1497-1505, doi: 10.1016/j.enconman.2009.02.019, 2009.
[70] M.A. Behrang, E. Assareh, A. Ghanbarzadeh and A.R. Noghrehabadi; “The potential of
different artificial neural network (ANN) techniques in daily global solar radiation
modeling based on meteorological data”; Solar Energy, Vol. 84, pp. 1468–80, 2010.
[71] Mazharul Islam; “Assessment of renewable energy resources of Bangladesh”. Available
at: http://www.sdnbd.org/sdi/issues/energy/publications/shakti-ebook1.pdf.