A Novel Ensemble Wind Speed Forecasting System Based on Artificial Neural Network for Intelligent Energy Management

Abstract

Accurate and consistent wind speed forecasting is vital for efficient energy management and the market economy. Wind speed is non-linear, non-stationary, and irregular, so it is very difficult to forecast. There are many forecasting methods currently in use; however, selecting and developing the most appropriate method for a particular region in wind speed forecasting is still a hot topic. This study presents a new and unique neural network-based ensemble system for forecasting wind speed, which is very difficult to predict but is directly related to the power generated by wind farms for individual and different sites. With the developed ensemble model, average mean absolute error, mean absolute percentage error and root mean square error values are obtained as 0.1269, 3.074%, 0.1596 respectively. Test results demonstrate significant contributions of the proposed system compared to existing statistical, heuristic and ensemble models, indicating that the developed model is a promising alternative for wind speed forecasting models. The obtained results show that this system is an effective and useful intelligent tool that can be used by various companies and government facilities that invest and operate in intelligent wind energy technologies.