The Scientific World Journal

Volume 2016 (2016), Article ID 9293529, 14 pages

http://dx.doi.org/10.1155/2016/9293529

## An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

Department of Electrical and Electronics Engineering, Anna University, Regional Campus Coimbatore, Coimbatore, Tamil Nadu 641 046, India

Received 26 October 2015; Revised 8 November 2015; Accepted 9 November 2015

Academic Editor: Syoji Kobashi

Copyright © 2016 V. Ranganayaki and S. N. Deepa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

#### 1. Introduction

For the past decade, the energy crisis is a major problem in various countries and the renewable energy utilization is becoming important all over the world. For the growing economy, the source of energy plays a major role and deriving the energy from the wind resources, which are available in plenty, will lead to developing an energy model and aid in appropriate allocation of the resources. Wind is a natural resource and form of renewable energy available in large. Wind energy is observed to be a clean energy and also it is pollution-free. Basically, wind is characterized by its direction, speed, and the time at which it occurs. Deriving wind energy from the natural wind flow is based on the force in which it moves or actually the speed of the wind. The force of wind or the wind speed is generally nonlinear and fluctuating in nature. In spite of its original nature, wind possesses the capability to generate the required amount of energy for the regular demands of the country. The prediction of wind speed is to be carried out for enhancing the energy generated [1]. Wind speed prediction compromises between the required demand and the generated energy. Wind speed predicted model designed with high accuracy and reliability acts as an effective tool to optimize the operating cost and improve the operational feature of the grid system.

This research paper contributes development of neural network (NN) models for performing effective wind speed prediction. Predicting wind speed is an important measure for wind energy connected grid systems. Few other factors that affect wind speed include humidity, moisture in the atmospheric air, atmospheric pressure, temperature, and rainfall. Predicting accurate wind speed enables the energy people to plan accordingly for the required energy demands. Various applications of wind speed prediction are energy to the grid, satellite and rocket launch, energy for agriculture, control module operations of military sectors, and so on. This prediction protects the generation of secured wind power and enables integrating the wind energy into electricity grids.

Over the years, it has been well noted that artificial neural network models are employed for various prediction applications [2]. Artificial neural network is a computational intelligent technique resembling the characteristic of the human biological neural network. The main characteristics of neural network include nonlinearity, adaptability, ability to handle large data, and nature of generalization. Due to these inbuilt features, neural network proves itself to be an effective tool for the accurate prediction of wind speed based on the defined input parameters. Neural networks have been applied in numerous fields like prediction, recognition, image processing, classification, association, control, and so on. Several approaches [3–5] are used to increase the accuracy of wind speed prediction, including physical and statistical methods. The physical method uses simple and higher order equations and involves physical quantities of the real time system. The statistical approaches carry out the relation between the existing and forecasted output whose parameters are estimated with the available data [6]. The statistical methods work on both linear and nonlinear models.

Generally, the wind speed is observed to vary rapidly due to its nonlinear nature. ANN being a flexible and an effective tool is employed in this research paper for predicting nonlinear behavior of the considered wind prediction system. The neural networks are basically inspired from the biological functioning of the human brain model comprising their fundamental element as the artificial neuron [7]. ANN does not require mathematical equations or mathematical model of the system but tends to minimize the error automatically based on the available knowledge of inputs and outputs. Thus, this paper mainly focused on wind speed prediction using neural network models. The performance metric considered is the mean square error value employed for measuring the quality of forecasted wind speed using the neural network. When the training process is initiated, the generalization performance is noted to differ with respect to time. Further, one of the major issues in the design of ANN is fixation of hidden neurons in the hidden layer. The presence of hidden layer and hidden neurons plays a major role for computing minimal error in artificial neural network modeling process of wind speed forecasting in renewable energy systems.

In this paper, the developed ensemble neural network model is tested with the proposed 102 criteria to fix the appropriate number of hidden neurons in the hidden layer of each MLP, Madaline, BPN, and PNN. The criteria are selected based on their satisfaction on the convergence theorem. The proposed ensemble neural network model is adapted in this research paper for application of wind speed forecasting. The main focus is to achieve the minimal error, improve the network stability, and better accuracy compared to other existing approaches [8] in order to assist planning, integration, and control of power system and wind farm.

#### 2. Related Work

The key objective of this research paper is to develop certain ensemble NN models and design the number of hidden neurons to be placed in hidden layer of the considered neuronal models and apply the developed model for accurate wind speed prediction. This contribution is evolved based on the detailed analysis carried out in earlier works related to this field and is presented in this section.

Numerous methods are reported in the literature for wind speed prediction such as physical approaches, time series, statistical methods, and machine learning approaches as well. A method for wind power generation employing BPN was developed in an effort to minimize the error and yield more accurate prediction [9]. Wind speed forecasting using a Recurrent Neural Network (RNN) model was modeled with an average prediction error below 10% [10]. An advanced online software platform for wind speed prediction has been modeled by Giebel [11]. A wind speed prediction up to 1 hour, 24 hours, and 48 hours based on MLP and Elman network has been proposed by Jayaraj et al. [12]. BPA (Back Propagation Algorithm) model has been developed for predicting wind speed twenty minutes in advance [13]. Silva et al. [14] presented a model employing Radial Basis Function Neural (RBFN) network for wind speed prediction and it has been found that it performs better than MLP. Zhang and Li [15] proposed ANN in hybridization with Field Programmable Gate Array (FPGA) network using state machine. Barbounis et al. [16] developed a long-term wind speed prediction with 72 hours ahead using RNN.

The MLP network for predicting wind speed at Zaragoza employed two models: time expert and spatial expert [17]. Wind speed prediction models using Taboo Search (TS) algorithm, recurrent fuzzy neural network, and adaptive neurofuzzy inference system have also been developed to enhance the accuracy of the estimated wind speed and to reduce the computation time [18–20]. Chen et al. [21] modeled a prediction system employing OLS (Orthogonal Least Squares) algorithm which measures the hidden nodes based on RBFN. This model predicted average hourly wind speed in one hour ahead. Wu et al. [22] developed a wind speed prediction from the previous values of the same variable using BPN. Monfared et al. [23] implemented a fuzzy based NN model for wind speed forecasting. This method provides a fuzzy associative memory table based on fuzzy logic and employs fast learning process for the neural network. Soman et al. [24] proposed three different models such as Adaptive Linear Network (Adaline), BPN, and hybrid network for prediction application. Han et al. [25] proposed a wind speed prediction method based on the improved neural network in which the wind direction factor was added as input vector by analyzing the relationship between wind direction and wind speed changes.

Bhaskar et al. [26] reviewed the present status of wind speed prediction which introduces the latest model of ANN, Support Vector Machine (SVM), and Particle Swarm Optimization (PSO). The work enabled the wind farm owners to understand the current wind prediction model capabilities. Fesharaki et al. [27] proposed a model employing adaptive weighted PSO with ANN for wind speed prediction. The wind speed prediction using genetic NN based on rough set theory was introduced by Guo et al. [28]. A multiple architecture system for wind speed prediction based on MLP and RBFN has also been developed [29]. Terzi et al. [30] proposed a new hybrid model which uses ANFIS and BPN for wind speed prediction. Sajedi et al. [31] proposed a model which uses ANFIS for short-term wind speed prediction. A hybrid model is developed employing RBFN and persistence method for wind speed forecasting. The RBFN model with Grubbs test is developed by Wu et al. [32]. Shi et al. [33] proposed a hybrid forecasting model which consists of ARIMA-NN and ARIMA-SVM. Cao et al. [34] proposed a model based on RNN with five different heights of wind mill. Xinrong et al. [35] modeled a Relevance Vector Machine (RVM) and Empirical Mode Decomposition (EMD) based wind speed forecasting model. Hu et al. [36] proposed a pattern based approach for short-term wind prediction to do better than the clustering based approach. Zhang et al. [37] carried out work on a hybrid wind speed forecasting based on intelligent optimized algorithm. Based on the review made with regard to the different neural network models employed for wind speed prediction, the identified limitations include the following:(i)Certain approaches developed for predicting wind speed employed random number of hidden neurons in the hidden layer; this resulted in either overfitting or underfitting problem.(ii)Error has not been significantly decreased in the earlier methods.(iii)Existing methodologies employ trial procedures and in certain cases numbers of hidden neurons are not fixed.Thus this paper focuses on developing ensemble neural network model combining the features of MLP, BPN, Madaline, and PNN to predict the wind speed for the collected real time dataset samples. Criteria are evolved satisfying the convergence theorem to fix the accurate number of hidden neurons for each of the ensembles and the average error is reduced to a possible minimal level during the training process of the developed ensemble neural network model.

#### 3. Problem Formulation

The wind power generated by a wind farm is critically based on the stochastic nature of wind speed and an unexpected deviation in the wind power output results in increase in the operating costs of the electrical system under consideration. The relation between wind speed and wind power is highly nonlinear in nature. Thus presence of error in wind speed prediction will also generate a large error in wind power generation. This technique practically improves the rate of performance. Generally, the neural network learns from the past data and with that experience predicts the future data. An accurate wind speed prediction model will allow grid operations to operate economically to meet the demands of the needful electrical customers. Hence accurate and reliable wind speed prediction is a prerequisite for good grid operation and advanced control strategy. The behavior of the wind speed is nonstationary and this indicates dynamic property during different period resulting in variations of input and output [38–41].

Considering the above facts, the problem to be addressed in this paper includes formulating suitable criterion to fix the number of hidden neurons for the proposed ensemble neuronal model so as to predict wind speed with higher accuracy rate and minimal error. No proper fixation of hidden neurons for the neural network models may increase the computational time and may delay the convergence of the network increasing the error rate. Thus hidden neuron selection plays a major role in neural network modeling process and this is carried out on evaluation of the generalization error during the learning process. When a smaller number of hidden neurons are placed in the model, this may result in local minima problem and when a bigger number of hidden neurons are placed, this may lead to instability of the neuronal model. Thus a trade-off should be maintained in proposing the criterion to fix the number of hidden neurons in the hidden layer of the proposed ensemble neuronal model.

The performance of the proposed ensemble neural network model is determined by the minimal mean square error (MSE). So the mean square error is used as the performance metric for performing the learning process for wind speed prediction. To determine the optimal ensemble NN model, MSE criteria are employed and are defined by the following equation: where specifies the predicted output, indicates the actual output, and denotes the number of samples. The perfect design of ensemble neural network architecture is highly important for the challenge of better accuracy in predictive models.

#### 4. Modeling the Proposed Ensemble Neural Network Architecture

This section presents the proposed modeling of ensemble neural networks to be employed for wind speed prediction in renewable energy systems. Basically, in neural network modeling there exist no specific methodologies to select the number of hidden neurons to be located in the hidden layer. In this paper, certain new criteria are evolved to fix the hidden neurons for the ensemble network using the mathematical foundation of convergence theorem. Each and every criterion that satisfies the convergence theorem is tested for its optimality at the training process for reduction of errors. The criterion that achieves minimal error satisfying the convergence theorem is chosen to be the optimal criterion to be considered for fixing hidden neurons in the proposed ensemble model. The criteria are formulated based on the number of input layer neurons (). The basic block diagram of the proposed ensemble neural network is as shown in Figure 1.