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

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.

A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods

“…Further, the prediction accuracy of the proposed method is compared with seven distinct state-of-the-art methods used for short-term wind-power prediction applications with similar time horizons. The performance of the proposed method is compared with ARIMA [11,27], Persistence Model (PM) [28,29], Nonlinear AutoRegressive eXogenous model (NARX) [30], SVM [31,32], and Multilayer Perceptron neural network (MLP) [33], Extreme Learning Machine neural network (ELM) [34], and PSF [25] models for each week's dataset from all four seasons, as well as for the one-year dataset. All comparisons are performed for 5, 15, 30, and 60 min ahead of value prediction.…”

Estimation of target station data using satellite data and deep learning algorithms

“…The arbitrary selection the number of neurons in ANN produces insufficient results. 4 Also, the input elements used in training of the network have a great impact on results. Direction input is a very important input that can cause a 23% forecast effect on complex terrains.…”

“…For example, the parameters created in the model must be carefully selected. The arbitrary selection the number of neurons in ANN produces insufficient results 4 . Also, the input elements used in training of the network have a great impact on results.…”

Wind potential assessment for an efficient wind farm sizing

“…The forecasting methods of wind speed must be accurate to provide pragmatic wind speed. Many methods have been adopted in literature, such as artificial neural networks (ANNs) that use the propagation algorithm, fuzzy logic (Alexiadis et al, 1999; Damousis et al, 2004; Lawan et al, 2014; Ranganayaki and Deepa, 2016; Zhang et al, 2014), Kalman filter (Song et al, 2016), and also the kernel methods known by their generalization ability (Bouzgou, 2014). Commonly, wind speed is described by means of the Weibull function.…”