| 引用本文: | 肖白,张婕,姜卓,施永刚,焦明曦,王徭.基于秩次集对分析理论的空间负荷预测方法[J].电力自动化设备,2020,40(4): |
| XIAO Bai,ZHANG Jie,JIANG Zhuo,SHI Yonggang,JIAO Mingxi,WANG Yao.Spatial load forecasting method based on rank set pair analysis[J].Electric Power Automation Equipment,2020,40(4): |
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| 摘要: |
| 利用秩次集对分析理论在处理系统不确定性方面的优势，提出一种新的空间负荷预测方法。首先，在电力地理信息系统中，根据待预测区域内各10 kV馈线供电范围生成Ⅰ类元胞，将Ⅰ类元胞的历史负荷数据分别按不同的集合容量生成多个历史数据集合和1个目标数据集合；其次，对各历史数据集合进行秩次变换得到相应的秩次集合，并分别将其与目标数据秩次集合构成集对；然后，寻找与目标数据集合相似的历史数据集合，选取相对误差最小的集合容量对应的预测值作为各Ⅰ类元胞负荷预测值；最后，以等大小网格生成Ⅱ类元胞，根据Ⅰ类元胞负荷预测值结合用地信息求出各Ⅱ类元胞的负荷预测值，从而得到网格化后的空间负荷预测结果。工程实例验证了所提方法的实用性和有效性。 |
| 关键词: 空间负荷预测 地理信息系统 秩次集对分析 分类负荷密度 元胞 |
| DOI：10.16081/j.epae.202002033 |
| 分类号:TM714 |
| 基金项目:吉林省产业创新专项基金资助项目(2019C058-7)；吉林省教育厅科技项目(JJKH20180442KJ) |
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| Spatial load forecasting method based on rank set pair analysis |
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XIAO Bai1, ZHANG Jie1, JIANG Zhuo2, SHI Yonggang3, JIAO Mingxi4, WANG Yao4
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1.School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;2.School of Computer Science and Technology, Beihua University, Jilin 132021, China;3.Tonghua Power Supply Company of State Grid Jilin Electric Power Company Co.,Ltd.,Tonghua 134001, China;4.Changchun Power Supply Company of State Grid Jilin Electric Power Company Co.,Ltd.,Changchun 130021, China
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| Abstract: |
| According to the advantages of R-SPA(Rank Set Pair Analysis) theory in dealing with uncertainties of system, a novel SLF(Spatial Load Forecasting) method is proposed. Firstly, Class Ⅰ cells are generated according to the power supply range of each 10 kV feeder in the area to be forecasted in the power GIS(Geographic Information System),and several historical load sets and one target data set are generated from the historical load data of Class Ⅰ cells according to different set capacities. Secondly, the corresponding rank sets are obtained by rank transformation of the historical data sets, which are combined with the target data to form the set pairs. Then, the historical data set that is similar to the target data set is searched, and the forecasting values of set capacity with minimum relative errors are taken as the load foresting values of Class Ⅰ cells. Finally, Class Ⅱ cells are generated by equal size grid, and their load forecasting values are solved according to the load forecasting values of Class Ⅰ cells and land use information, thus the spatial load forecasting results after gridding are obtained. The practicability and effectiveness of the proposed method are verified by an engineering example. |
| Key words: SLF GIS rank set pair analysis classified load density cell |
