A Lifting Spatial Resources Matching Approach Based Wind Power Prediction of Regions

doi:10.3969/j.issn.1000-7229.2017.07.002

Abstract

Abstract: ABSTRACT： Wind power prediction of large scale wind farm clusters will contribute to the scientific and reasonable power generation schedule establishment and enhance the robustness of the power grid. Spatial resources matching algorithm （SRMA） based wind power prediction of regions is with higher prediction accuracy and less computing time than the method of up-scaling approach, which is widely adopted by industrial companies. However, there is only one matching parameter of the SRMA method, which restricts the further improvement of the prediction accuracy. This paper presents an improved SRMA method, which contains the parameter of the historical wind power output, based on the introduction of the SRMA method. Then, this paper predicts the wind power within 0~12 hours with the data derived from one wind farm cluster which contains 52 wind farms. The results show that, the prediction accuracy of the improved SRMA method within 4 hours is higher than that of the traditional SRMA method, and is applicable for industrial application.

Key words: wind power prediction of regions, spatial resources matching algorithm （SRMA）, matching parameters, parameter optimization

CLC Number:

TM 711

PENG Xiaosheng，FAN Wenhan，WANG Bo，ZHANG Tao，WEN Jinyu，DENG Diyuan，XIONG Lei，CHE Jianfeng. A Lifting Spatial Resources Matching Approach Based Wind Power Prediction of Regions

[J]. Electric Power Construction, 2017, 38(7): 10-.

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