A GA-PSO Hybrid Algorithm Based Neural Network Modeling Technique for Short-term Wind Power Forecasting

Abstract

This article proposes a hybrid neural network modeling technique for forecasting of wind power generation based on an integrated algorithm combining genetic algorithm (GA) and particle swarm optimization (PSO). The share of wind energy in electric power generation keeps growing supported by favorable environmental policies aiming at achieving low-emission targets. However, due to the intermittent and uncertain nature of wind flow, integration of wind power into electric power systems brings operational challenges to address. Accurate wind power generation forecasting tools play a key role to address the challenges. A multi-layered feed-forward artificial neural network model optimized by a combination of genetic algorithm and particle swarm optimization algorithm is developed in this work for wind power generation forecasting. The proposed technique is tested based on practical information obtained from Goldwind Smart Microgrid in Beijing. The performance of the proposed method is superior to neural network models optimized using GA and PSO separately, as well as the benchmark persistence approach.