Spatial Load Forecasting Method Based on Support Vector Machine and Internet Information Correction

doi:10.11930/j.issn.1004-9649.201807020

Abstract

Abstract: In order to improve the spatial load forecasting accuracy of power system planning, a spatial load forecasting (SLF) method is proposed based on support vector machine (SVM) and Internet information correction. The method can be divided into three steps: firstly, based on k-means clustering analysis, the SVM model is used to get the initial prediction value of the block load; secondly, the deviation between the actual load value and the predicted load value can be calculated based on the historical data of the block load; and thirdly, those uncertain events that causes deviation, including the newly increasing load events in the cells and the mutation of revenue growth rate of the enterprises in the cells, are identified by Internet information that is obtained by search engine. The impact of the uncertain events on spatial load is qualitatively analyzed, and a quantitative impact model is established for classifying the events between these two events and their effects. Based on the model, the initial forecasting value of the load is corrected, and the final forecasting load values of the blocks in the planning area is obtained. A case study of an area in Beijing shows that this method can improve the prediction accuracy and can be used for spatial load forecasting in distribution network and energy Internet planning.

Key words: power system analysis, spatial load forecasting, k-means clustering, support vector machine, Internet information

CLC Number:

TM715

GUO Yanfei, CHENG Lin, LI Hongtao, RAO Qiang, LIU Manjun. Spatial Load Forecasting Method Based on Support Vector Machine and Internet Information Correction[J]. Electric Power, 2019, 52(4): 80-88.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201807020

https://www.electricpower.com.cn/EN/Y2019/V52/I4/80

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