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Wind power as a renewable source of energy, has numerous economic,environmental and social benefits. In order to enhance and control renewablewind power, it is vital to utilize models that predict wind speed with highaccuracy. Due to neglecting of requirement and significance of datapreprocessing and disregarding the inadequacy of using a single predictingmodel, many traditional models have poor performance in wind speed prediction.In the current study, for predicting wind speed at target stations in the northof Iran, the combination of a multi-layer perceptron model (MLP) with the WhaleOptimization Algorithm (WOA) used to build new method (MLP-WOA) with a limitedset of data (2004-2014). Then, the MLP-WOA model was utilized at each of theten target stations, with the nine stations for training and tenth station fortesting (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh,Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of thesubsequent hybrid model. The capability of the hybrid model in wind speedforecasting at each target station was compared with the MLP model without theWOA optimizer. To determine definite results, numerous statistical performanceswere utilized. For all ten target stations, the MLP-WOA model had preciseoutcomes than the standalone MLP model. The hybrid model had acceptableperformances with lower amounts of the RMSE, SI and RE parameters and highervalues of NSE, WI, and KGE parameters. It was concluded that the WOAoptimization algorithm can improve the prediction accuracy of MLP model and maybe recommended for accurate wind speed prediction.

Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm