Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method

doi:10.11930/j.issn.1004-9649.201907013

Abstract

Abstract: Regional wind power and PV(photovoltaic) power forecast is an effective way to improve the robustness of power grid, however, the traditional single-station power accumulation method is poor in accuracy and operation efficiency, because the construction time of each station is different and the accuracy of single-station is various. Therefore, this paper proposes a method for short-term regional wind and PV power prediction based on feature clustering. Firstly, the machine learning-based Bisecting K-Means(BKM) clustering algorithm is used to reasonably divide the wind farms and PV stations in the region into clusters; Secondly, based on the correlation between of the historical power data of each power station and the total historical power data in the region, a representative power station is selected for each region; Thirdly, after optimizing and correcting the NWP(numerical weather prediction) model of each representative power station, a short-term power prediction framework model is established using BP neural network based on the cluster division of wind farms and PV power plants. The result shows that the proposed method is higher than or close to the traditional single-station power accumulation method in short-term prediction accuracy, but it can significantly improve the modeling efficiency while ensuring the prediction accuracy.

Key words: wind farm, PV station, cluster division, short-term power forecast, bisecting K-means

CUI Yang, CHEN Zhenghong, XU Peihua. Short-Term Power Prediction for Wind Farm and Solar Plant Clusters Based on Machine Learning Method[J]. Electric Power, 2020, 53(3): 1-7.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201907013

https://www.electricpower.com.cn/EN/Y2020/V53/I3/1

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