Hourly wind speed forecasting based on support vector machine and artificial neural networks

Abstract

Wind speed is the main component of wind power. Therefore, wind speed forecasting is of big importance due to its uses. It permits to plan the dispatch, determine the hours of storage needed, the amount of energy stored that should be used and avoid the big fluctuations in the electrical grid caused by the nature of the renewable energy resources. In this paper, we propose four hybrid models based on Support Vector Machine (SVM) and Artificial Neural Networks (ANNs) or just Neural Networks (NN) for wind speed forecasting. Using the Ordinary Least Squares (OLS) analysis for selecting the parameters more influencing wind speed. Then, a Support Vector Machine and Artificial Neural Networks models are tuned by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of these models is evaluated using three statistical indicators: the Mean Square Error (MSE), Mean Error (ME) and Mean Absolute Error (MAE). The results show a better performance of the neural model compared to the support vector machine.