Performance analysis of artificial and wavelet neural networks for short term wind speed prediction

Abstract

In recent years, the importance of integrating the production of wind energy into electrical energy networks has been increasing rapidly. The biggest challenge to integrate wind energy into the power grid wind power is variability and discontinuity. To deal with this situation, the best approach is to predict future values of wind power production. Wind speed estimation methods with high accuracy are an effective tool that can be used to minimize these problems. This paper presents a short-term wind speed prediction using artificial neural network (ANN) and wavelet neural network (WNN) and compares the performance of these networks. Data are collected from a weather station located in Ondokuz Mayis University in ten minute resolution for a period of one year. Wind speed predictions are presented within a period of 24-hours for 10 minute ahead. Although ANN and WNN use the same topology, the performance of the proposed prediction system based on WNN has higher than that of ANN. The root mean square error (RMSE) and the mean squared error (MSE) values have been selected as performance criteria.