Preprint Article Version 1 This version is not peer-reviewed

Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm

Version 1 : Received: 14 February 2020 / Approved: 17 February 2020 / Online: 17 February 2020 (02:22:05 CET)

How to cite: Samadianfard, S.; Hashemi, S.; Kargar, K.; Izadyar, M.; Mostafaeipour, A.; Mosavi, A.; Nabipour, N. Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm. Preprints 2020, 2020020233 (doi: 10.20944/preprints202002.0233.v1). Samadianfard, S.; Hashemi, S.; Kargar, K.; Izadyar, M.; Mostafaeipour, A.; Mosavi, A.; Nabipour, N. Wind Speed Prediction Using a Hybrid Model of the Multi-Layer Perceptron and Whale Optimization Algorithm. Preprints 2020, 2020020233 (doi: 10.20944/preprints202002.0233.v1).

Abstract

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control the renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh and Deylaman) to increase the accuracy of the subsequent hybrid model. Capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI and KGE parameters. It was concluded that WOA optimization algorithm can improve prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.

Subject Areas

wind power; machine learning; hybrid model; prediction; whale optimization algorithm

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