Wind Power Forecasting using A Heterogeneous Ensemble of Decomposition-based NNRW Techniques

Abstract

Accurate and reliable wind power forecasting plays a vital role in the operation and management of power systems. Hence, it has become necessary to research and develop a high-accuracy wind power forecasting model. However, owing to highly nonlinear and non-stationary patterns of wind power time-series, creating a wind forecasting model capable of predicting such series accurately is both complicated and challenging. Aiming at this challenge, this paper introduces a new decomposition-based hybrid model based on multiple decomposition techniques, neural network with random weights (NNRW), and linear combiner. In our approach, the original time-series is decomposed into a collection of sub-series by different decomposition techniques. Each sub-series is modeled and predicted separately using NNRW. The predicted signals of each decomposition model are then reconstructed independently. Finally, all of the reconstructed results are integrated by the combiner using a linear combination method. The predictive performance of the proposed method was compared with other state-of-the-art techniques in over 12 wind power time-series. The experimental results show that the predictive performance of the proposed method remarkably outperforms the other competitors, proving the developed model to be effective, efficient, and practicable.