chapter 2 a review on wind power...
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CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 23
CHAPTER 2
A REVIEW ON WIND POWER FORECASTING
Forecasting of wind power can be performed using several techniques.
Forecasting of the wind speed is a primary step involved in forecasting of wind power.
The wind speed forecast is used for predicting wind power using the manufacturer’s
power curve or any other wind turbine power curve model. Hence, in this chapter, a
review of existing wind speed forecasting models, wind turbine power curve models
and wind power forecasting models have been presented.
Part of the work reported in this chapter has been published as under:
1. Lydia. M., Suresh Kumar. S., Immanuel Selvakumar. A. and Edwin Prem Kumar. G.
(2014) “A Comprehensive Review on Wind Turbine Power Curve Modeling
Techniques”, Elsevier - Journal of Renewable and Sustainable Energy Reviews, 30,
452 - 460 (Impact Factor: 5.627) doi: 10.1016/j.rser.2013.10.030
2. Lydia. M. and Suresh Kumar. S. (2010) “Wind Farm Power Prediction : An
Overview” Fifth International Symposium on Computational Wind Energy, CWE2010,
23-27 May 2010, Chapel Hill, North Carolina, USA.
ftp://ftp.atdd.noaa.gov/pub/cwe2010/Files/Papers/291_edwin.pdf
3. Lydia. M. and Suresh Kumar. S. (2010) “A Comprehensive Overview on Wind Power
Forecasting” 9th
International Power and Energy Conference IPEC 2010, 27-29
October 2010, Suntec, Singapore, 268-273. DOI: 10.1109/IPECON.2010.5697118
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 24
2.1 REVIEW ON WIND SPEED FORECASTING TECHNIQUES
Forecasting of wind speed has become important in the recent days.
According to Abdel-Aal (2009), wind speed forecasts play a vital role for the operation
and maintenance of wind farms and for integration into power grids as well as in
shipping and aviation. Availability of accurate wind forecasts will definitely go a long
way in improving the security of power grid, increasing the stability of power system
operation and market economics and aid greatly in enhancing the penetration of wind
power (Abdel-Aal, 2009). This will definitely result in large scale reduction of
greenhouse gas emissions and the other pollutants emitted during the consumption of
the depleting conventional energy sources. Wind speed forecasting models aid in
effective management and safety of port areas too (Solari et al., 2012).
Wind speed forecasting models have been developed based on several
statistical and learning approaches.
2.1.1 Statistical Techniques
The wind speed probability distribution describes the likelihood that certain
values of wind speed will occur. The probability distributions are generally
characterized by probability density function (pdf) or a cumulative density function
(cdf)) (Manwell, 2009). The two commonly used probability distributions in wind data
analysis are the Rayleigh and Weibull distribution. Rayleigh distribution requires only
the knowledge of mean wind speed ( y ) and hence is the simplest velocity probability
distribution. The pdf and cdf of Rayleigh distribution is given below:
2
2 4exp
2 y
y
y
ypdf
-----(2.1)
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 25
2
4exp1
y
ycdf
-----(2.2)
The pdf and cdf of Weibull distribution is given in Equation (2.3) and Equation (2.4)
kk
c
y
c
y
c
kpdf exp
1
-----(2.3)
k
c
ycdf exp1 -----(2.4)
where k is the shape factor and c is the scale factor. Higher the value of k, lesser is the
observed wind speed variation. Olaofe and Folly (2013) carried out wind resource
assessment of their test site using Weibull’s and Rayleigh’s distribution.
The other statistical techniques used to forecast wind speed include Auto
Regressive Moving Average (ARMA), Empirical Mode Decomposition (EMD)
technique etc. Kavasseri and Seetharaman (2009) highlight the fact that accurate wind
speed forecasts are essential to schedule dispatchable generation and tariffs in the day-
ahead electricity market. They have developed fractional Auto Regressive Integrated
Moving Average (ARIMA) models to predict wind speed on 24h and 48h horizons.
The results suggested that this method was able to improve the forecasting accuracy by
an average of 42% compared to the persistence method. A linear prediction model for
wind speed forecasting has been developed based on current data and the previous one
/ and two years data corresponding to the same period (El-Fouly et al 2008). The wind
speeds predicted by this model showed significant improvement up to 54.4% for MAE
and 55.3% for RMSE.
A hybrid high-precision forecasting method based on EMD and time-series
analysis has been developed by Liu et al (2009). Li and Wang (2008) have developed
an EMD-ARMA model for short-term wind forecasting for wind farms. Erdem and
Shi (2011) predicted the tuple of wind speed and direction using ARMA based
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 26
approaches. Guo, Wu, Lu and Wang (2011) developed a new hybrid wind speed
forecasting method to forecast the daily average wind speed one year ahead. They
eliminated the seasonal effects from actual wind speed datasets using seasonal
exponential adjustment and used the back propagation neural network for prediction.
Gomes and Castro (2012) have developed forecasting models for wind speed and
power using ARMA and ANN (Artificial Neural Networks). They concluded that
ARMA models performed better than ANN but were slightly time consuming.
Wavelet transformation is a tool for time-frequency analysis. It decomposes
the highly non-linear wind speed time series into several approximate stationary time-
series (Lei and Ran 2008). A hybrid model based on wavelet-decomposition and
ARMA for short-term wind speed prediction of wind farm has been presented by Lei
and Ran (2008). Liu, Tian, Chen and Li (2010) proposed a hybrid statistical method to
predict wind speed and power. They developed a new short-term forecasting method
based on wavelets and classical time series analysis.
2.1.2 Soft-Computing Techniques
Barbounis and Theocharis (2007) employed local recurrent neural networks
for wind speed prediction using spatial correlation. The prediction performance was
improved by using online learning algorithms based on the recursive prediction error
approach. A hybrid model for short-term wind speed forecasting was developed using
Hyperbolic Tangent Unit (HTU)-based NN by Hervas-Martinez et al (2009). These
networks were trained using evolutionary algorithms and they outperformed the
conventional ANN like Multi-Layer Perceptron (MLP). Shuang et al (2007) developed
a wind speed forecasting model developed using ANN based on Tabu search
algorithm.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 27
An ANN-based wind speed forecasting model has been developed by
Cadenas and Rivera (2009) and the performance of this model was tested with different
architectures and the simplest model with two layers, two input neurons and one output
neuron gave good results with MSE and MAE of 0.16% and 3.99% respectively. Wind
speed forecasting model was developed using three different NN types namely
adaptive linear element, back propagation and Radial Basis Function Network (RBFN)
by Li and Shi (2010). The performance of these models was evaluated using MAE,
MSE and MAPE. Tran et al (2009) developed a wind speed forecasting model based
on wavelet transform and cascade-correlation neural networks. Short-term wind speed
prediction model based on evolutionary support vector regression algorithms was
developed by Salcedo-Sanz et al (2011). A multiple architecture system for the
prediction of wind speed has been developed by Bouzgou and Benoudjit (2011).
Different regression algorithms like MLP, RBFN and Support Vector Machine (SVM)
has been used to build the model and three fusion strategies have been employed
namely simple, weighted and non-linear.
A short-term wind forecasting method based on Gaussian process, making
use of kernel machine technique and Bayesian estimation has been presented by Mori
and Kurata (2008). It reduced 27% and 12% of average error for MLP and RBFN
respectively. It also reduced the maximum error by 13% and 7.8% for MLP and RBFN
respectively. Abdel-Aal (2009) proposed the use of abductive networks based on the
group method of data handling, to model and forecast the mean hourly wind speed.
Monfared, Rastegar and Kojabadi (2009) developed a new technique for wind speed
forecasting based on fuzzy logic and artificial neural networks. Kusiak and Li (2010)
developed a data-driven approach for estimation of wind speed. They concluded that
for a given training and testing scenario, better prediction accuracy depended on higher
Pearson’s correlation coefficient.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 28
Shi, Guo and Zheng (2012) have evaluated hybrid prediction techniques for
wind speed and power generation. The performance of two hybrid models, namely
ARIMA-ANN and ARIMA-SVM have been compared with the single ARIMA, ANN
and SVM forecasting models. It was observed that they don’t produce better
forecasting performance consistently for different forecasting time horizons. A
recurrent neural network based wind speed forecasting model has been developed by
Cao, Ewing and Thompson (2012).
Guo, Zhao, Lu and Wang (2012) developed a modified empirical mode
decomposition based ANN model for multi-step prediction of wind speed. Vaccaro,
Bontempi, Taieb and Villacci (2012) developed multiple-step ahead wind speed
prediction models based on adaptive local learning techniques. A spectral and a
multifractal analysis of time-series wind speed data were performed on 412 data each
of duration 350s, sampled at 20 Hz and its effect on wind energy production was
investigated (Calif and Schmitt, 2012). Time series wind speed data has a fractal
character and could be modelled using Weierstrass function fitted by genetic algorithm
(Barszcz et al., 2012). The main objective was to use it as a model of load of wind
turbine gears for simulation of different operational conditions for wind turbine
vibration modeling. Gan et al., (2012) developed a novel method of wind speed
prediction based on Mycielski algorithm. The wind speed values are converted to wind
states in order to apply this algorithm.
A combination forecasting model comprising of time series and back
propagation neural network prediction model for short-term wind speed prediction has
been developed (Nan et al., 2013). A wind speed forecasting bias correction method
based on empirical orthogonal function has also been proposed. Liu et al., (2013) have
developed three hybrid models for wind speed forecasting, based on the theories of
wavelet, wavelet packet, time- series analysis and artificial neural networks. The
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 29
performance of these models have been compared with some classical prediction
techniques including Adaptive Neuro-Fuzzy Inference System (ANFIS), wavelet
packet – RBF (Radial Basis Function) and persistence method. Jiang, Song and Kusiak
(2013) have developed a time-series forecasting model based on Bayesian theory and
structural break modeling. This Bayesian structural break model predicts wind speed
as a set of possible values, which could be used for wind turbine predictive control and
wind power scheduling.
2.2 REVIEW ON WIND TURBINE POWER CURVE MODELING
TECHNIQUES
A Wind Turbine Power Curve (WTPC) can go a long way in providing
accurate forecasting of wind power. The output power of a wind turbine significantly
varies with wind speed and hence every wind turbine has a very unique power
performance curve. A power curve aids in wind energy prediction without the technical
details of the components of the wind turbine generating system (Manwell et al. 2009).
The electrical power output as a function of the hub height wind speed is captured by
the power curve. Power curves for existing machines, derived using field tests, can be
obtained from the wind turbine manufacturers. The approximate shape of the power
curve for a given machine can also be estimated using the power characteristics of
rotor, generator, gearbox ratio and efficiencies of various components. The conversion
of power in the wind into actual power varies non-linearly because of the transfer
functions of available generators (Monteiro et al. 2009).
The data required for modeling a power curve is the wind speed and power
output recorded at periodic intervals over a long time. The historical data could either
be obtained from experimental wind farms or from the SCADA system. Once the
required data is available, the energy production of the wind turbine can be analyzed
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 30
using four different approaches namely, direct use of data averaged over a short time
interval, the method of bins, development of velocity and power curves from data and
statistical analysis using summary measures (Manwell et al. 2009).
2.2.1 IEC Power Curve
The International Standard IEC 61400-12-1 has been prepared by the
International Electrotechnical Commission (IEC) technical committee 88: Wind
turbines. The standard methodology for measuring the power performance
characteristics of a single wind turbine has been specified here. It is also applicable for
testing the performance of wind turbines of varied sizes and types. It can be used to
evaluate the performance of specific turbines at specific locations and also aid in
comparing the performance of different turbine models or settings (IEC 61400-12-1,
2005).
The power performance characteristics of wind turbines are ascertained by
the measured power curve and the estimated annual energy production. Simultaneous
measurements of wind speed and power output is made at a test site for sufficiently
long duration to create a significant database under varying atmospheric conditions.
The measured power curve is determined from this database. The annual energy
production is calculated, assuming 100% availability, by applying the measured power
curve to reference wind speed frequency distributions.
The measured power curve is determined by applying the “method of bins”,
for the normalized datasets using the following equations:
iN
j ji
i
i yN
y1 ,,n
1 -----(2.5)
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 31
iN
j ji
i
i PN
P1 ,,n
1 -----(2.6)
where yi is the normalized and averaged wind speed in bin i, yn,i,j is the normalized
wind speed of dataset j in bin i, Pi is the normalized and averaged power output in bin
i, Pn,i,j is the normalized power output of dataset j in bin i and Ni is the number of
10min data sets in bin i. The accuracy of WTPC models have improved by using the
profile information available using remote sensing instruments (Wagner and Courtney
2009). However, it has been stated by Trivellato et al. (2012), that the IEC-based
power curve gives the behavior of the wind turbine with the influence of site
turbulence. Though the current site data is rendered with reliable accuracy, the IEC
power curve contains the hidden effect of current site turbulence, in such a way that its
blind application to other sites is not very correct. The IEC procedure also ignores the
fast wind fluctuations through the 10 min averaging and the results in obtaining the
behavior of the machine independent of wind fluctuations. Hence the need for
modeling site-specific WTPC has gained great significance.
2.2.2 Power Curve Modeling Objectives
A WTPC modeled from the measured data in a particular site using better
modeling techniques will definitely overcome the drawbacks posed by the
manufacturer provided power curve and the IEC power curve. A power curve modeled
from the measured data deviates when some power outputs are negative implying wind
turbine is consuming energy due to low wind speed and some power outputs vary even
when the wind speed is constant. Hence it is necessary that a power curve is modeled
with minimum error. The objective for modeling a WTPC is four fold: wind energy
assessment and prediction, choice of wind turbines, monitoring and troubleshooting
and finally predictive control and optimization of wind turbine performance (Figure
2.1).
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 32
Fig. 2.1 WTPC Modeling Objectives
Wind Energy Assessment and Prediction
Wind resource assessment is the process by which wind farm developers
estimate the future energy production of a wind farm. Accurate assessments are crucial
to the successful development of wind farms. The meteorological potential of any
candidate site is equivalent to the available wind resource (Manwell et al. 2009). If the
wind speed data of the site is available, a WTPC can facilitate the estimation of wind
energy that can be produced over a period of time. Accurate WTPC models also help
in the planned expansion of wind farms (Norgaard and Holttinen, 2004). An analytical
method to estimate the output power variation in a wind farm has been devised using
dynamic power curves by Jin and Tian (2010).
Estimating and controlling the variability of wind farm power output aids in
providing stable wind power to the utility/grid and improves loss of load expectation
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 33
(LOLE). Olaofe and Folly (2013) have concluded that the analysis of the energy
outputs of the wind turbines based on the developed site power curves is more accurate
than the turbine power curves. The WTPC models can very well be used for wind
power forecasting at varying time horizons. Accurate forecasting of wind power in
intra-day and day-ahead electricity markets are the need of the day. The power curve of
a variable speed wind turbine has been modified using a new curve called the
controllers power curve to account for the wind dynamics and has resulted in more
accurate power prediction (Zamani et al. 2007).
Choice of Wind Turbines
WTPC models aid the wind farm developers to choose the generators of
their choice, which would provide optimum efficiency and improved performance.
The impact of WTPC on the cost of energy and optimal system configuration in a small
wind off-grid power system has been presented by Simic and Mikulicic (2007).
Judicious choice of a wind turbine generator that yields higher energy at higher
capacity factor can be done by using the normalized power curves proposed by
Jangamshetti and Rau (2001). These generalized curves, obtained from a new ranking
parameter known as Wind Turbine Performance Index, can be used at the planning and
development stages of wind power stations. The wind turbine capacity factor was
modeled using the site wind speed and turbine power curve parameters by Albadi and
El-Saadany (2009, 2010). An increase in energy yield up to 5% was obtained, when
the proposed model was used for optimum turbine-site matching.
Monitoring and Troubleshooting
A WTPC model can serve as a very effective performance monitoring tool
(Kusiak et al. 2009c). The model developed can be used as a reference for monitoring
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 34
the performance of wind turbines. An equivalent steady state model of a wind farm
under normal operating conditions has been realized using data-driven approach and
has been utilized for creation of quality control charts, with the aim of detecting
anomalous functioning conditions of the wind farms (Marvuglia and Messineo. 2012).
Monitoring the performance of a wind farm using three different operational curves has
been discussed by Kusiak and Verma (2013). The WTPC has been used to identify
various faults and its severity by Kusiak and Li (2011). The wind turbine power output
has been evaluated and deviations that may result in financial losses are calculated
using online monitoring of power curves (Schlechtingen 2013). The performance of
four different data mining approaches has been compared for this purpose.
Predictive Control and Optimization
Uluyol et al (2011), showed that the WTPC can be very useful for
performance assessment and for generation of robust indicators for component
diagnostics and prognostics. Higher reliability and lower maintenance costs can be
incurred by employing condition-based rather than hour-based monitoring. A copula
model of WTPC has been used for early identification and detection of incipient faults
such as blade degradation, yaw and pitch errors (Gill et al. 2012). The copula-power
curve condition monitoring correlates faults or anomalies to statistical signatures.
Kusiak and Li (2011) have shown that power curve models along with data mining
based model extraction could be used to predict specific faults with an accuracy of
60minutes before they occur.
Wind farm power curves are adversely affected by the changing
environmental and topographical conditions. Equivalent power curve models
incorporating the effect of array efficiency, high wind speed cut out, topographic
effect, spatial averaging, availability and electrical losses have been developed by
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 35
McLean (2008). The impact of wind speed reduction due to the wakes created by the
wind turbines upstream determines the array efficiency. The main factors affecting
array efficiency are wind farm layout, wind regime and the type of terrain. Offshore
wind farms are susceptible to a higher wake loss. The effect of topography is higher in
upland wind farms than the low land wind farms, because of the greater variation in
wind speed. This can be reduced by averaging the power from a range of power curves
at different wind speeds. An equivalent regional power curve is produced for each
wind farm by averaging, in order to reduce the variation of wind speeds experienced by
wind farms across a region. Availability and electrical efficiency of offshore sites are
generally lower than onshore sites.
The effects of the environmental parameters on wind turbine power
probability density function curve were studied by Jafarian et al (2008). These
parameters included the annual average wind speed, k-factor of Weibull distribution,
autocorrelation factor, diurnal pattern strength, altitude above sea level and variance of
monthly averaged wind speed in one year. It was found out that the altitude above sea
level (which determines the air density indirectly) and the k-factor of Weibull
distribution affected the wind turbine output power more than all the remaining
parameters.
Site-specific adjustments are required by wind turbine power curves in order
to address the effects of turbulence, complex terrain, wind shear, blade fouling and
icing, power curve measurement blockage effects and uncertainty in availability of
wind farms (Tindal et al. 2008). It was observed that for a site with 18% turbulence, a
1% reduction of energy took place. Hence for sites, where the predicted turbulence
levels and wake effects are more than 15%, a turbulence power curve adjustment factor
should be applied. For complex terrains, it was suggested that an up-flow power curve
adjustment factor be applied. To account for the uncertainty in power curve and wind
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 36
turbine availability, an allowance equivalent to 2% of the wind farm energy production
has also been suggested. As the output power of wind turbine varies as the cube of the
input wind speed, it is the variability in the wind speed that affects the power curve
most. If the annual mean wind speed varies by 10%, it was observed that the
corresponding variation in available wind energy was about 25% (Khalfallah and
Koliub 2007a).
The most important criteria to be addressed while formulating the various
techniques for WTPC modeling, is the model accuracy. Different performance
metrics have been used by various researchers. The most common metrics are AE, RE,
MAE, RMSE, sMAPE, NMAPE, coefficient of determination R2 etc.
2.2.3 Power Curve Modeling Techniques
A critical analysis of the various methods used of mathematical modeling of
wind turbines has been presented by Thapar et al. (2011). The two different kinds of
models developed by them are models based on fundamental equation of power
available in the wind and models based on the concept of power curve of the turbine.
It was concluded that models based on the equation of power were very cumbersome.
Models based on the power curve of the turbines gave fairly accurate results. The
different techniques available in literature for WTPC modeling have been classified
into parametric techniques and non-parametric techniques as shown in Figure 2.2.
Parametric Techniques
Parametric techniques are based on solving mathematical modeling
expressions. The actual wind turbine generator power output (Pa) can be expressed as
given below:
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 37
syy
ry
rP
ryy
cyyp
syy
cyy
ya
P )(
,0
)( -----(2.7)
where y is the wind speed, yc is the cut-in speed, yr is the rated speed and ys is the cut-
out speed, p(y) is the linear variable region between the cut-in speed and rated speed
and Pr is the rated power.
Fig. 2.2 WTPC Modeling Techniques
A) Linearized Segmented Model
This is the simplest parametric model where piecewise approximation of the
WTPC has been carried out using the equation of a straight line (Khalfallah and
Koliub, 2007).
Parametric Modeling Techniques
Linearized
Segmented Model
Polynomial Power Curve
Maximum Principle Method
Dynamical Power Curve
Probabilistic Model
Ideal Power Curve
4-Parameter Logistic Function
Non-Parametric Modeling Techniques
Copula Power Curve
Cubic Spline Interpolation
Neural Networks
Fuzzy Methods
Data Mining Algorithms
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 38
cmyP ------(2.8)
where P is the output power and y is the wind speed, m is the slope of the segment and
c is any constant (Figure 2.3). The data is fitted on to the linear segments using the
method of least squares, which estimates the coefficients by minimizing the summed
square of residuals. The residual of the ith
data point ri is defined as the difference
between the actual power output Pa(i) and the fitted response value Pe(i), and is
identified as the error associated with the data. The summed square of residuals (S) is
given by
N
i
ea
N
i
i iPiPrS1
2
1
2 ))()(( -----(2.9)
The least squares criterion assumes that the wind measures or forecasts are
error-free, which is never true in practice. This problem could be overcome by using
the Total Least Square (TLS) criterion, in which the contribution of the noise
components in both power and meteorological variables are accounted for in the model
parameter estimation (Pinson et al. 2008).
Fig. 2.3 Linearized Segmented Model
Power (kW)
Wind speed (m/s)
P4 = P5
P3
P2
P1 y1 y2 u3 y4 y5
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 39
B) Polynomial Power Curve
A WTPC has been modeled using polynomial expressions of varied orders
in different literatures (Jafarian and Ranjbar, 2010). Seven different models were used
to model the linear region of the wind turbine power curve by Akdag and Guler (2010)
and their energy output yields were calculated. A review of four commonly used
equations for representation of power curves of variable speed wind turbines namely
polynomial power curve, exponential power curve, cubic power curve and approximate
cubic power curve has been done by Carillo et al. (2013). All these four equations
have been used to model the linear region of the WTPC.
Quadratic Power Curve
Carillo et al. (2013) have used a second degree polynomial expression for
modeling the WTPC.
2
321)( ycyccup -----(2.10)
Where c1, c2 and c3 are constants determined from yc, ys and Pr. A WTPC based on the
method of least squares, using quadratic expressions for the linear region has been
presented by Thapar et al (2011). Three different quadratic expressions have been used
to approximate the linear region guaranteeing better accuracy.
s
c
yyyforcycyc
yyyforcycyc
yyyforcycyc
yp
23332
2
31
212322
2
21
11312
2
11
)( -----(2.11)
where c11, c12, c13, c21, c22, c23, c31, c32, c33 are coefficients of the quadratic equation and
y1 and y2 and wind speeds at heights h1 (m) and h2 (m) respectively.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 40
Cubic Power Curve
A WTPC has been modeled using a cubic power expression by Carillo et al
(2013).
3
,2
1)( yACyp eqp -----(2.12)
where Cp,eq is a constant equivalent to the power coefficient. Model for WTPC based
on cubic law has also been used (Thapar et al. 2011). Since the fraction of the wind
power that gets converted to electrical power depends on several parameters like wind
speed, rotational speed of the turbine, angle of attack, pitch angle, mechanical and
electrical efficiencies, the accuracy decreases.
Approximate Cubic Power Curve
An approximate cubic power curve model has been derived by assigning
maximum value to the power coefficient (Cp,max) (Carillo et al. 2013).
3
max,2
1)( yACyp p -----(2.13)
Exponential Power Curve
The variable speed WTPC can be modeled using an exponential equation as
given below (Carillo et al. 2013):
)(2
1)( cp yyAKyp -----(2.14)
where Kp and are constants.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 41
Ninth Degree Polynomial
The performance of polynomial models of fourth degree, seventh degree
and ninth degree has been compared using curve fitting toolbox of MATLAB (Raj
MSM et al, 2011). It was observed that the ninth degree polynomial given by Equation
(2.15) performed better than the other equations.
)(2
1)( cp yyAKyp -----(2.15)
where c1,…,c10 are constants. The shapes of the WTPC of different turbines with
varied design ratings are different. Hence, the major disadvantage of the polynomial
models is that there can never be a unique single set of generalized characteristic
equations that can be used for all types of turbines.
Model based on Weibull’s Parameters
A WTPC based on Weibull’s parameters has been used by Thapar et al
(2011) but the accuracy of modeling was very poor.
kbyayp )( -----(2.16)
where k
r
k
c
k
cr
yy
yPa
and
k
c
k
r
r
yy
Pb
It was observed that the model based on Weibull’s parameters lacked accuracy in the
range of cut-in to rated speed since Equation (2.16) did not accurately represent the
wind turbine power curve shape in that range (Thapar et al, 2011).
Among the polynomial based power curves, the quadratic power curve
showed the worst results due to its sensitivity to the data given by the manufacturer and
the approximate cubic power curve recorded a better performance (Carillo et al 2013).
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 42
However Raj et al (2011) observed that a polynomial of higher degree recorded better
performance.
C) Maximum Principle Method
The maximum principle method proposed by Rauh defines an empirical
power curve using a very simple method (Gottschall and Peinke 2008). The power
curve is defined by the location, where, in a given wind speed bin, the maximal density
of points Pi is found i.e. the power curve is given by the points{yj, Pk(j)}, where j is the
number of the speed bin and k(j) denotes the power bin with
i
jikik yyPPN )())(: and -----(2.17)
kjk NN )( -----(2.18)
where )(x is a Heaviside function defined by
else
widthbinparticularthewithxifx
0
221)( -----(2.19)
However, Gottschall and Peinke (2008) proved that Rauh’s method of maximum
principle overestimated the points in the region of transition to the rated power in the
WTPC and the accuracy of the method was also not good.
D) Dynamical Power Curve
Determination of WTPC through a dynamical approach has been presented
using the Langevin Model by Gottschall and Peinke (2008). The main objective of this
method is to separate the dynamics of the wind turbine power output into two parts: a
deterministic and a stochastic part. The deterministic part corresponds to the actual
behavior of the wind turbine and the stochastic part corresponds to other external
factors such as the wind turbulence. The wind turbine power output is described as a
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 43
stochastic process that satisfies the Markovian property and hence can be separated
into a drift and a diffusion part.
)()()( tpyPtP stat -----(2.20)
where P(t) is the time series power data, Pstat is the stationary power value dependent
on the wind speed y and p(t) corresponds to short-time fluctuations around this value
caused by wind turbulence. The performance of the dynamical power curve was
compared with the IEC power curve and the maximum principle method according to
Rauh and was found to be much accurate.
The advantages of the dynamical power are that it could extract the
dynamical behaviour of any wind turbine with better accuracy and produce machine-
specific and site-independent results. Measurements taken for a short-time is enough
for this approach, where as the IEC power curve procedure requires long-term data and
also averages out all the dynamics (Milan 2008).
E) Probabilistic Model
A WTPC modeled using polynomial expressions is deterministic in nature,
since the relationship with the output power and the input wind speed is pronounced by
the modeling expressions. Jin and Tian (2010) proposed a probabilistic model for
WTPC as follows:
3)( yCyp p -----(2.21)
In this model, the wind turbine output power is a random number whose value is
determined by y, the wind speed and , the variation of the power output. This model
characterizes the dynamics of wind energy production and estimates the uncertainty in
wind power when the wind turbine generators operate in the region between cut-in and
rated wind speed. The wind turbine power is assumed to follow the normal distribution
with a varying mean and constant standard deviation.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 44
F) Ideal Power Curve
The ideal power curve, as proposed by Trivellato et al (2012), describes the
intrinsic performance of the turbine, eliminating the hidden effect of the site
turbulence. The ideal power curve refers to ideal conditions such as steady and laminar
flow of wind, absence of yaw error and steady state power output. Assessment of wind
energy available in a test site and extension of power curve to sites with different
turbulence levels are the main applications of the ideal power curve. It is analytically
derived by a Taylor’s expansion and uses an accurate assumption of the ideal power
coefficient. The convergence of the Taylor’s expansion has been improved by
applying the Shanks’ transformation. This ideal power curve was successfully
compared with the IEC power curve. The calculation of annual energy estimated using
the ideal power curve was well within the inherent experimental error.
G) Four Parameter Logistic Function
The shape of the power curve is similar to the four parameter logistic
function and hence WTPC models have been developed based on this by Kusiak et al
(2009b, 2009c)
)11( yy nemeaP -----(2.22)
The vector parameters of the logistic function, a, m, n and determine its shape. The
parameters of the logistic function have been estimated using algorithms like least
squares, maximum likelihood and Evolutionary Programming (EP) (Kusiak et al
2009b).
Non-Parametric Techniques
Non-parametric techniques are used to solve the following equation:
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 45
)(yfP -----(2.23)
Several non-parametric methods have been used to find the relationship between the
input wind speed data and output power. A brief description of such techniques used
to model the WTPC has been given below:
A) Copula Power Curve Model
Copula is a distribution function in statistics and is used to describe the
dependence between random variables. A copula model of wind turbine performance
has been developed by Gill et al (2012) and Stephen et al (2011). This method
includes the measures of uncertainty while estimating the performance and also allows
comparison of inter-plant performance. A copula representation of a WTPC is
constructed by considering the power curve to be a bivariate joint distribution. To
make sure that the transformed variables have uniform distribution, accurate estimation
of wind speed and power marginals are essential. An estimated power curve copula is
shown as a non-parametric probability density estimate by Gill et al (2012). But this
approach can be made fully useful, only if a more advanced method of parametric
estimation of marginals and dependency is in place which may take the form of a
mixture density estimate of the marginals and cubic spline estimate of the copula. This
would aid in capturing and identifying changes in the operating regime also.
B) Cubic Spline Interpolation Technique
Interpolants and smoothing spline are the non-parametric fitting techniques
used to draw a simple, smooth curve through the data (Curve fitting Toolbox, 2002).
Interpolation is the process of estimating values that lie between two known data
points. The different kinds of interpolant methods include linear interpolation, nearest
neighbor interpolation, cubic-spline and Piece-wise Cubic Hermite Interpolation
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 46
(PCHIP). The WTPC model has been approximated using the cubic-spline interpolate
on technique by Thapar et al (2011). This method fits a different cubic polynomial
between each pair of data points. The method of least squares and cubic spline
interpolation performed extremely well for wind turbines with smooth power curve.
C) Neural Networks
An Artificial Neural Network (ANN) is an information-processing model
simulating the operation of the biological nervous system. It has a significant capacity
to derive meaning from complicated or imprecise data and finds application in
extraction of patterns and detection of trends that are too complex to be identified by
humans (Sivanandam and Deepa, 2010).
Under normal conditions, the equivalent steady state model of wind farm
has been realized using three different neural network models namely, Generalized
Mapping Regressor (GMR), a feed-forward Multi Layer Perceptron (MLP) and a
general regression neural network (GRNN) by Marvuglia and Messineo (2012). GMR
is a novel incremental self-organizing competitive network. NN models like radial
basis network and generalized regression network was used for estimation of annual
energy by Jafarian and Ranjbar (2010).
D) Fuzzy Methods
Fuzzy logic is basically a multi-valued logic which deals with approximate
reasoning. Fuzzy logic based on Takagi-Sugeno model was used to model the annual
wind energy produced by Jafarian and Ranjbar (2010). Modeling of WTPC using
fuzzy based methods includes fuzzy cluster center method, fuzzy c-means clustering
and subtractive clustering.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 47
Fuzzy Cluster Center Method
Ustunas and Sahin proposed the application of fuzzy model based on cluster
center estimation to WTPC modeling (Ustuntas and Sahin, 2008). The wind turbine
power generation data are clustered and the cluster centers are determined using the
model algorithm. The more the number of clusters, higher is the accuracy of the
technique. The performance of the fuzzy cluster center method is better than the least
squares method.
Fuzzy C-Means Clustering
A WTPC model has been done using Fuzzy C-Means (FCM) clustering
algorithm by Raj et al (2011). Unlike K-means clustering, FCM eliminates the effect
of hard membership. It employs fuzzy measures as the basis for calculation of
membership matrix and identification of cluster centers, permitting data points to have
different degrees of membership to each of the clusters (Hammouda and Karray).
Fuzzy clustering and similarity theory have been applied by Suhua et al (2008) to
classify the measured wind speed data from different time. A fixed output value is
chosen to represent the wind turbine output power in that category.
Subtractive Clustering
Subtractive clustering algorithm has been used for modeling WTPC by Raj
et al (2011). This algorithm is very similar to mountain clustering, but the density
function is calculated only at every data point, instead of at every grid point
(Hammouda and Karray, 2000). The number of computations is reduced significantly,
since the data points themselves become cluster centers. According to Raj et al (2011),
the fuzzy cluster center method modeled the WTPC better than the other techniques.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 48
E) Data Mining Algorithms
Data mining is all about solving problems and extracting valuable
information and patterns by analyzing data present in huge databases. The huge
volumes of data stored in the SCADA systems of wind farms present a priceless
opportunity for the application of data mining algorithms for wind turbine technology.
Non-parametric models of a WTPC have been obtained using five data mining
algorithms namely multi-layer Perceptron (MLP), random forest, M5P tree, boosting
algorithm and k-Nearest Neighbor (k-NN) by Kusiak et al (2009b). Among all these,
the k-NN algorithms performed better. The different parametric and non-parametric
methodologies employed by researchers for modeling of WTPC ultimately aim at
capturing the wind turbine performance accurately and thus use for energy prediction,
monitoring and predictive control of wind turbine operation.
2.3 REVIEW ON WIND POWER FORECASTING TECHNIQUES
The stochastic and random nature of wind makes it a necessity for novel
modeling approaches with improved performance. The wind industry needs accurate,
sophisticated models for forecasting of power and condition monitoring of wind farms.
These models involve large number of parameters and hence development of such
models is really a challenging task (Kusiak et al, 2009c). The techniques used for wind
power forecasting can be classified into three approaches: Physical approach,
Statistical approach and Learning approach. The different physical processes involved
in a wind farm namely, the wind conditions at the site, hub height of the turbines, wind
farm shading effects, turbine power curve, Model Output Statistics etc. are modeled in
the physical approach (Ernst et al, 2008). Figure 2.4 shows the main steps of the
physical approach based wind power forecasting.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 49
Fig. 2.4. Physical Approach of Wind Power Forecasting
In statistical approach, the relationship between weather forecasts and
power production is described using explicit statistical analysis. This non-linear and
highly complex relationship is represented using suitable algorithms in the learning
approach. Evolutionary algorithms and other common soft- computing techniques
neural network, fuzzy logic etc. have also been combined to obtain more sophisticated
and improved models. An in-depth review of the current methods and advances in
wind power forecasting and prediction has been presented by Foley et al (2012). A
review on the developments of wind power prediction models to meet the offshore
requirements, performance of forecasting models and the importance of wind power
trading has been presented by Kariniotakis et al (2004).
Any advanced forecasting technique is implemented only if it outperforms
the conventional persistence model. The persistence model is a naive prediction
model, which assumes that the wind in the next time step will be the same as that
which occurred in the present time step (Monteiro et al 2009).
Wind Farm &
Terrain
Characteristics
PHYSICAL
MODEL
SCADA
Data NWP
(Atmospheric
Variables)
Wind
Generation
Forecast
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 50
2.3.1 Physical Approach
Numerical Weather Prediction (NWP) uses mathematical models to describe
physical processes. The physical approach consists of several submodels, which
contain the mathematical description of the physical processes relevant to the
translation. In this approach, the NWP forecasts are provided by the global model to
several nodes across a particular area (Monteiro et al 2009). The wind forecast at
certain grid point and model is translated using submodels, to power forecast at the
considered site and at a particular turbine height.
A power prediction model based on the forecasts from High Resolution
Limited Area Model (HIRLAM) of the Danish Meteorological Institute has been
developed for wind farms connected to electricity grid by Landberg (1999). An online
automatic prediction system for wind farm production output based on HIRLAM
model has been presented by Landberg (2001). The performance of the Eta model in
wind forecasting has been found out by Lazic et al (2010) using four common
measures of accuracy namely the mean difference, mean absolute difference, root mean
square difference and correlation coefficient. The possibility of integration of a short
term wind forecasting system with the grid in Romania has been explored using the
Weather Research and Forecasting (WRF) model and the Global Forecast System
(GFS) model (Dica et al 2009).
To avoid the forecasting errors of this approach, the power predictions are
post-processed using Model Output Statistics. The important disadvantage of this
approach is that it requires huge amount of high quality, online or offline measured
data.
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 51
2.3.2 Statistical Approach
The statistical approach involves the direct transformation of the input
variables into wind generation which takes place in the statistical block. Figure 2.5
shows the main steps of the statistical approach. The statistical block can combine
various inputs from the NWP model along with the data from the online SCADA data
for the estimation of wind power over a region. The statistical block can include one or
several linear or non-linear statistical models like Auto Regression (AR), Auto
Regressive Moving Average (ARMA) method, probability density function etc. With
these models, a direct estimation of regional wind power is possible from the input
parameters in a single step. A review of the statistical methods used for wind power
forecasting has been presented here.
Fig. 2.5 Statistical Approach of Wind Power Forecasting
Accurate wind power forecasts are increasingly important for integration of
wind energy to electricity grids. For a region with several distributed wind farms, a
STATISTICAL
MODEL
SCADA Data
NWP
(Atmospheric
Variables)
Wind
Generation
Forecast
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 52
method to make aggregated wind power predictions based on distances between
weather forecasting vectors has been presented by Lobo and Sanchez (2009). A wind
power forecasting method based on ARMA is developed by Rajagopalan and Santoso
(2009). The relationship between forecast accuracy and wind power variability has
also been studied. The feasibility of a comparatively low cost statistical model that
does not require any data beyond the historic power generation data, which might be
less accurate but of very high use for smaller wind farms have been explored by
Milligan et al (2003). The local variables of wind speed and direction has been
incorporated to statistically model the nonlinearities related to wind physics and other
complex dynamics and a benchmarking has been carried out between the regime-
switching and conditional parametric models (Gallego et al 2011). A hybrid statistical
method to predict wind speed and wind power, based on wavelets and classical time
series analysis has been presented by Liu et al (2010). The mean relative error of this
method is very small for multi-step forecasting and is robust in dealing with jumping
data.
Empirical Mode Decomposition (EMD) is a technique for analyzing non-
linear, non-stationary signal. It identifies the intrinsic oscillatory modes in a data
empirically by their characteristic time scales and then divides the data into Intrinsic
Mode Functions components. Short-term prediction of wind power using chaotic
theory and EMD has been presented by An et al (2012).
Systems with partially unknown information about its parameters and
characteristics are known as grey systems. A grey predictor model requires reduced
number of historical data, adapts itself to the dynamic behavior of the data and also
requires lesser processing time (El-Fouly et al 2007). These characteristics make it an
ideal choice for wind speed forecasting applications. A Grey predictor model for one-
step ahead average hourly wind speed forecasting and power prediction has been
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 53
developed by El-Fouly et al (2007). Guo (2009) developed a new Maximum Power
Point Tracking (MPPT) strategy based on grey wind speed prediction. This method
gives improved performance by reducing the search area and search time of the MPPT
process. A novel technique for wind speed forecasting and power prediction using
GM(1,1) model has been presented by El-Fouly et al (2006). Figure 2.6 shows the
different steps performed on the data using grey predictor model. AGO stands for
Accumulated Generating Operation and IAGO stands for Inverse Accumulated
Generating Operation. This technique showed an average accuracy of 11.2% better
than the persistent model for wind speed forecast and 12.2% for output power
prediction. An et al (2011) developed a wind farm power forecasting model based on
the combination of wavelet transform, chaotic time series and GM(1,1) method. The
actual wind turbine power output time series is pre-processed and decomposed using
wavelet transforms. The chaotic property of the data is identified and then future wind
farm power is predicted using GM(1,1) method.
Probabilistic forecasts provide the future probability of one or more events
(Juban et al 2007). The nature of the forecast variable can either be discrete or
continuous. A technique for producing the complete predictive probability density
function for wind power based on kernel density estimation methods have been
proposed by Juban et al (2007). Ensemble prediction systems give an assessment of
weather uncertainty using sophisticated estimate of the probability density function for
weather variables. The potential of weather ensemble predictions for wind power
forecasting has been explored by Taylor et al (2009).
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 54
Fig. 2.6 Grey Predictor Model GM (1,1) for Forecasting
Wind power prediction at a wind farm level is contaminated with two types
of uncertainty: one from the process of wind prediction and the other from the complex
wind farm structure and terrain characteristics. Hence adoption of entropy related
concepts for training mappers like neural networks for wind power prediction shows
significant improvement over the traditional approach of variance-based criterion.
Wind power forecasting using entropy-based criteria algorithms have been developed
by Bessa et al (2008a). Bessa et al (2008b) presents the significant improvements
obtained in wind power forecasting based on information entropy-related concepts.
Renyi’s entropy is combined with a Parzen windows estimation of the error probability
density function to form the basis of two criteria namely minimum entropy and
maximum correntropy, for both online and offline training of neural networks used for
wind power prediction (Bessa et al 2009).
Original data series )0(X
Develop the AGO data series )1(X
Calculate the GM(1,1) parameters
Predict the future point )1(X̂
Apply the IAGO to the predicted
values for the AGO data series
Predicted values for the original data
series )0(X̂
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 55
2.3.3 Learning Approach
Learning approach includes the application of soft-computing techniques
like Artificial Neural Networks (ANN), fuzzy logic, Adaptive Neuro-Fuzzy Inference
System (ANFIS) and other hybrid algorithms. Forecasting models based on learning
approach, try to learn the relationship between the forecast speed or power and the
input variables.
A neural network is a massively parallel distributed processor that learns the
input-output mapping of variables, without explicitly deriving the model equations.
Kariniotakis et al (1996) presents the development of a prediction model for wind
power output profile, based on recurrent high order NN. An algorithm to optimize the
performance of the architecture of the forecasting model has also been presented and
this model showed significant improvement in performance over the persistence model.
Forecasting of mean hourly wind speed data using time series analysis has been
presented by Steftos (2002). The task was accomplished using linear ARIMA models
and feed forward artificial neural networks. The models were tested on two different
datasets and the ANN model outperformed the ARIMA model. An NN-based
forecasting model was developed by More and Deo (2003) and it was proved that these
models gave more accurate forecasts than the conventional time-series ARIMA model.
ANN-based spatial correlation model has been developed by Alexiadis et al (1999) and
it has been proved that these models outperform the persistence model by reducing the
average errors by 20-40%. Li et al (2001) developed a four input NN, whose
performance is better than the traditional single input models.
A local recurrent NN model trained using Recursive Prediction Error (RPE)
algorithm for forecasting of wind speed and power has been developed by Barbounis et
al (2006a) and Barbounis et al (2006b). Spatial information from remote measurement
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 56
stations have been incorporated in the recurrent NN models by Barbounis and
Theocharis (2007). A feed-forward NN model with back propagation algorithm has
been developed for prediction of wind energy output by Mabel and Fernandez (2008).
The wind energy output is predicted using the average wind speed, relative humidity
and generation hours as the input to the NN (Figure 2.7). The fifth generation
mesoscale model (MM5) has been hybridized by incorporating ANN for short-term
wind speed prediction by Salcedo-Sanz et al (2009). The MM5 model is a physical
model that performs downscaling of the data from the global model in order to obtain
wind speed prediction in a smaller area.
Fig. 2.7. ANN Architecture for Wind Forecasting
A fuzzy expert system can incorporate fuzzy if-then rules and provide fine-
tuning of the membership functions according to the input-output patterns (Figure 2.8).
A Takagi, Sugeno and Kang (TSK) fuzzy model for wind speed prediction and power
generation in wind parks has been presented by Damousis and Dokopoulos (2001).
The fuzzy model is trained using Genetic Algorithms (GA) and provides better speed
forecasts from 30 minutes to 2 h ahead (Damousis et al 2004).
*Wind Speed
*Relative
Humidity
*Generation
Hours
Wind
Energy
Forecast
Input
Layer
Hidden
Layer
Output
Layer
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 57
Fig. 2.8 Fuzzy Expert System for Wind Forecasting
ANFIS, which is the integration of a NN with a fuzzy inference system,
exploits the advantages of both. A neuro-fuzzy system, is in fact, a neural network that
is functionally equivalent to a fuzzy inference model. A very short-term wind
prediction for power generation using ANFIS has been presented by Potter and
Negnevitsky (2006). An advanced statistical method for wind power forecasting
based on RBFN, fuzzy logic and self-organized map has been presented by Sideratos
and Hatziargyriou (2007).
Data mining refers to extracting of knowledge from large amounts of data.
It enables us to discover interesting patterns from databases and information
repositories. Data mining finds extensive application in predictive modeling.
Predictive models can be realized using algorithms like NN, Bayesian classification,
Support Vector Machine (SVM) and decision tree methods. Data mining models for
short-term power prediction has been developed by Kusiak et al (2009a) and Kusiak et
al (2009d). Data mining and evolutionary computation based models for prediction
and monitoring have been developed by Kusiak et al (2009c). Kusiak et al (2010d)
dealt with the optimization of wind turbine power using data mining and evolutionary
computation algorithms. Prediction of wind turbine parameters using virtual models
was developed by Kusiak and Li (2010c). The performance of these models was
Local & Remote
Measurements
Database
Fuzzy
Expert
System
User Defined
Forecast Horizon
Wind Energy
Forecast
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 58
dependent on the input parameters selected and the data mining algorithms used for
extracting the model. The power ramp rates of a wind farm have been predicted using
data mining by Zheng and Kusiak (2009).
A short-term wind power forecasting model based on the combination of
NN and wavelet transform has been discussed by Catalao et al (2011a). A short-term
wind power forecasting model combining wavelet transform, Particle Swarm
Optimization (PSO) and ANFIS has been developed by Catalao et al (2011b). This
approach offered better prediction accuracy and reduced time of computation. A
clustering approach for short-term prediction of power has been presented by Kusiak
and Li (2010a).
A comparison between two different ensemble models for short-term wind
power forecasting using common verification indices and diagrams has been developed
by Alessandrini et al. (2013). In deterministic approach, it was observed that a higher
resolution of the ensemble system led to better results when compared with high
resolution deterministic model. Kalman filtering techniques have been employed to
enhance the prediction accuracy of wind speed and wind energy forecast of Numerical
Weather Prediction (NWP) models (Cassola and Burlando, 2012). Wind power
prediction based on numerical and statistical models have been discussed by
Stathopoulos et al. (2013). They concluded that accurate power prediction is possible
if the local atmospheric conditions are estimated correctly. Sideratos and
Hatziargyriou (2012) developed models for wind power forecasting with a focus on
extreme power systems events. A novel adaptive learning method for online training
of Radial Basis Function Neural Networks (RBFNNs) was also presented. Wind
power forecasting model based on wavelet decomposition and chaotic time series has
been presented by An et al. (2011). Kusiak, Zheng and Song (2009) developed wind
farm power prediction models based data mining algorithms. A hybrid model for
CHAPTER 2 – A REVIEW ON WIND POWER FORECASTING 59
short-term wind power prediction using empirical mode decomposition, chaotic theory
and grey theory was constructed by An et al (2010). Kusiak and Li (2010) developed a
short-term prediction model for power produced by wind turbines at low wind speeds
using clustering approach. Vaccaro et al. (2011) developed one-day- ahead wind power
forecasting models using data from multiple sources. It was proved that the
performance of this multi-model data fusion was better than the use of single-source
data. Catalao, Pousinho and Mendes (2011) proposed a hybrid method, combining
wavelet transform, PSO and an adaptive network based fuzzy inference system for
short-term prediction of wind power in Portugal.
Forecasting models developed based on learning approach and combination
of two or more approaches, give better and more accurate forecasts.
2.4 SUMMARY
Based on the exhaustive survey of literature on wind power forecasting,
three tasks have been carried out in this research work, namely the development of
Wind Speed Forecasting Models for different time horizons
Wind Turbine Power Curve Models
Wind Power Forecasting Models
An account of the various methodologies and models formulated in this regard has
been given in the subsequent chapters.