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

Abstract

Wind power as a renewable source of energy has numerous economic, environmental, and social benefits. To enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. 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) was 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. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP optimized by the Genetic Algorithm (MLP-GA) and standalone MLP 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 MLP-GA and standalone MLP model. In other words, the hybrid MLP-WOA models, with acceptable performances, reduced the RMSE values from 0.570∼2.995 to 0523∼2.751. Also, the obtained results indicated that the examined MLP-GA did not have a significant effect in increasing the estimation accuracy of standalone MLP models. It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.

Keywords

• Wind power
• Machine learning
• Hybrid model
• Prediction
• Whale optimization algorithm