MATEC Web Conf.
Volume 70, 20162016 The 3rd International Conference on Manufacturing and Industrial Technologies
|Number of page(s)||5|
|Published online||11 August 2016|
Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models
1 Electric Power Research Institute, Guangdong Power Grid Corporation, 510640, Guangzhou, China
2 School of Electric Power, South China University of Technology, 510640, Guangzhou, China
3 Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, 511458, Guangzhou, China
4 Guangdong Intework Energy Technology Co., Ltd, 511458, Guangzhou, China
Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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