A GA-PSO Hybrid Algorithm Based Neural Network Modeling Technique for Short-term Wind Power Forecasting. Issue 4 (1st September 2018)

• Record Type: Journal Article
• Authors: Semero, Yordanos Kassa; Zhang, Jianhua; Zheng, Dehua; Wei, Dan
• Is Part Of: Distributed generation and alternative energy journal. Volume 33:Issue 4(2018)
• Page Start: 26
• Page End: 43
• Publication Date: 2018-09-01
• Subjects: Wind -- Generation Forecasting -- Genetic Algorithm -- Particle Swarm Optimization -- Neural Networks; Distributed generation of electric power -- Periodicals; Cogeneration of electric power and heat -- Periodicals; Renewable energy sources -- Technological innovations -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; Environmental protection -- Periodicals; 333.7932
• DOI: 10.1080/21563306.2018.12029913
• Languages: English
• ISSNs: 2156-3306

Abstract

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.