A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique

As a dominant form of renewable energy sources with significant technical progress over the past decades, wind power is increasingly integrated into power grids. Wind is chaotic, random and irregular. For proper planning and operation of power systems with high wind power penetration, accurate wind speed forecasting is essential. In this paper, a novel hybrid Neural Network (NN)-based day-ahead (24 hour horizon) wind speed forecasting is proposed, where five hybrid neural network algorithms are evaluated. The five algorithms include Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA), WNN trained by Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM)-based neural network, Radial Basis Function (RBF) neural network, and Multi-Layer Perceptron (MLP) Neural Network. Single- and multi-features and their effect on the accuracy of wind speed prediction are also analyzed. The wind speed dataset used in this paper is Saskatchewan's recorded historical wind speed data. Despite the excellent wind power potential, only 6.5% of the total electricity demand is currently supplied by wind power in Saskatchewan, Canada. This study paves the way for economical operation, planning, and optimization of Saskatchewan's future wind power generation.

Automated summary

Wind power, as a dominant form of renewable energy, is increasingly integrated into power grids with significant technical progress. Accurate wind speed forecasting is crucial for the proper planning and operation of high wind power penetration systems. This paper proposes a novel Neural Network-based method for day-ahead wind speed forecasting, evaluating five different algorithms and analyzing the impact of single- and multi-features on prediction accuracy.