Abstract— A new strategy in wind speed prediction based on adaptive weighted particle swarm optimization combined with artificial neural networks was proposed. Regarding the data gathering, sometimes it is difficult to provide the neural network with sufficient data to be trained efficiently. In order to solve this problem Adaptive weighed particle swarm optimization is used to increase the data the produced data is fed to a multilayered feed forward neural network to predict the future wind speed. This method has lead to good estimated wind speed accuracy and good prediction performance.

Keywords- Adaptive Weighted Particle Swarm Optimization; Multilayered Feed Forward Neural Network; Predictio; Wind Speed; Data Scarcity.

I. INTRODUCTION

The increased use of energy and the depletion of the fossil fuel reserves combined with the increase of the environmental pollution have encouraged the search for clean and pollution-free sources of energy [1]. One of these is wind energy. This is a clean, inexhaustible and a free source of energy that has served the mankind for many centuries by propelling ships, driving wind turbines to grind grains and for pumping water. Despite the high cost of wind power this may become a major source of energy in the years to come. This is so because the severe pollution of the planet originating from the burning of the fossil fuels and the nuclear energy risks cannot continue forever [3].

The predicted variations of meteorological parameters such as wind speed, relative humidity, solar radiation, air temperature, etc. are needed in the renewable industry for design, performance analysis, and running cost estimation of these systems.

For proper and efficient utilization of wind power, it is important to know the statistical characteristics, persistence, availability, diurnal variation, and prediction of wind speed.

The wind characteristics are needed for site selection,

performance prediction and planning of wind turbines. Of these characteristics, the prediction of mean monthly and daily wind speed is very important [2, 5].

The neural network has several advantages. It needs sufficient data to learn the relation between inputs and outputs. One problem that exists is the data’s scarcity and this problem is solved by producing as much data as it is needed between the available values of the data using AWPSO. It can be said that the way applies a neural network to the problem like an open algorithm, or the problem from which a situation changes in the time, is suitable. Moreover, since a neural network does parallel processing to a mathematics model carrying out calculation processing of series, even if some noises and the error are contained in input data, an output can take out an approximation solution [4].

This paper is organized in this way. In section 2 the applied method and the used data are introduced. In section 3 the simulation results are explained and finally section 4 gives the conclusion.

II. THE APPLIED METHOD

In this study the available data is the twenty months of data containing wind speed and direction recorded by cup anemometer at a level of 80 feet from ground. These data are collected at Khoramdareh in Iran.

In order to solve the data scarcity problem AWPSO is used to create some data between each available value of the data. 80 percent of the data produced is used for training the neural network and the rest is used for test data. In order to get accurate results the data is normalized and finally the trained network is used to produce the prediction data using the actual set of data that was the original data. The prediction is a one-step-ahead prediction that means n data is given to the network and the data n+1 is supposed to be predicted. In order to predict k data the inputs have to be fed

Using AWPSO to Solve the Data Scarcity Problem in Wind Speed Prediction by Artificial Neural Networks

Mohsen Fesharaki1, Niusha Shafiabady1, Mohsen A. Fesharaki1, Shahab Ahmadi1

1 Science Society of Mechatronics, Azad University, Science and Research Branch

mnfesharaki@yahoo.com , nshafiabady@yahoo.com, fesharaki@yahoo.com, shahab_ahmadi_01@yahoo.com

2010 International Conference on Artificial Intelligence and Computational Intelligence

978-0-7695-4225-6/10 $26.00 © 2010 IEEE

DOI 10.1109/AICI.2010.251

49

2010 International Conference on Artificial Intelligence and Computational Intelligence

978-0-7695-4225-6/10 $26.00 © 2010 IEEE

DOI 10.1109/AICI.2010.251

49

k times and the k data should be predicted in k steps.

A. The Applied Neural Network

A neural network method is developed to retrieve ground surface wind speed. The used neural network is the MLP network. The neural network is a fully connected, forward feeding, multilayered perception that consists of one input layer, two hidden layers and one output layer. Levenberg-Marquardt is used for training data in the present study. Fig. 1 shows the MLP network’s structure. The input layer has five neurons that correspond to the wind speed given. The first hidden layer has 10 neurons with tansig transfer functions and the second hidden layer has 5 neurons again with tansig transfer functions and finally the output layer has one neuron with linear transfer function. As it is seen in Fig.

1, five inputs are fed to the network and the 6th

data is predicted that is a one step ahead prediction.

B. AWPSO

PSO (Particle Swarm Optimization) is a population-based method used for optimization. It is an evolutionary computation technique developed by Kennedy and Eberhart. It seems like an appropriate time to step back and look at where we are, how we got here, and where we think we may be going. It is written from an engineering and computer science perspective, and is not meant to be comprehensive in areas such as the social sciences. The applications already developed include human tremor analysis, power system load stabilization, and product mix optimization and many others. AWPSO is a verification of PSO to have a better performance. The formulation of AWPSOis as follows as mentioned is (1) and (2).

Nt , 2, 1,t t/Nt a a

) w-(1r ww

1)-(tx(t)v(t)x

(t))] x-(xr(t)) x-(x[r a1)-(t v w (t)v

0

0 0

iii

iiglobalbest2ilocalbesti1ii

(1)

1] [0, wand 1] [0.5, is a

U(0,1) r ,rr,

00

21 (2)

In (1), Nt is the maximum number of iterations and t is the current iteration.

III. SIMULATION RESULTS

The simulations are done using Matlab and its results are given in Fig. 2~6. As mentioned before the data is increased by adding 30 data between each two actual data we have, using AWPSO and then it is normalized as shown in (3).

New_Data = (Old_Data-min) / (max-min) (3)

Fig. 2 - The result of the normalized train data fed to the neural network

About 80 percent of the total produced data is used for training the network and its result is given in Fig. 2 and Fig. 3 shows the same data that has been denormalized too. As the figure shows the network has been trained well. The RSME of the network is given in table I. The rest of the produced data is used as the test input for the neural network. Its result is given in Fig. 4 and its denormalized

Fig. 1 - The structure of the used MLP neural network

 

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