Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
Focusing on short-term wind power forecast, a method based on the combination of Genetic Algorithm (GA) and Extreme Learning Machine (ELM) has been proposed. Firstly, the GA was used to prepossess the data and effectively extract the input of model in feature space. Basis on this, the ELM was used to establish the forecast model for short-term wind power. | 48 The Open Electrical Electronic Engineering Journal 2017 11 48-56 Send Orders for Reprints to reprints@benthamscience.ae BENTHAM OPEN CrossMark The Open Electrical Electronic Engineering Journal Content list available at www.benthamopen.com TOEEJ DOI 10.2174 1874129001711010048 RESEARCH ARTICLE Short-term Wind Power Prediction Using GA-ELM Xinyou Wang1 Chenhua Wang2 and Qing Li3 1 Institute of Technology Gansu Radio TV University Lanzhou 730030 P.R. China 2 Northwest Engineering Corporation Limited PowerChina Xi an 710065 P.R. China 3 State Grid Xinjiang Electric Power Company Electric Power Research Institute Grid technology Center Urumqi 830000 P.R. China Received May 11 2016 Revised December 13 2016 Accepted December 14 2016 Abstract Focusing on short-term wind power forecast a method based on the combination of Genetic Algorithm GA and Extreme Learning Machine ELM has been proposed. Firstly the GA was used to prepossess the data and effectively extract the input of model in feature space. Basis on this the ELM was used to establish the forecast model for short-term wind power. Then the GA was used to optimize the activation function of hidden layer nodes the offset the input weights and the regularization coefficient of extreme learning thus obtaining the GA-ELM algorithm. Finally the GA-ELM was applied to the short-term wind power forecast for a certain area. Compared with single ELM Elman algorithms the experimental results show that the GA-ELM algorithm has higher prediction accuracy and better ability for generalization. Keywords Short-term prediction Wind power prediction Genetic algorithm Extreme learning machine GA-ELM NWP. INTRODUCTION Wind power as a green renewable energy resource has gained more and more significance in the recent years around the world. With the rising wind power capacity in wind farm the penetration of wind resources in power system has been increasing in the recent years. However wind power is characterized as intermittent with
