基于支持向量机和互联网信息修正的空间负荷预测方法

doi:10.11930/j.issn.1004-9649.201807020

中国电力 ›› 2019, Vol. 52 ›› Issue (4): 80-88.DOI: 10.11930/j.issn.1004-9649.201807020

基于支持向量机和互联网信息修正的空间负荷预测方法

郭艳飞¹, 程林¹, 李洪涛², 饶强², 刘满君¹

1. 清华大学电机工程与应用电机技术系, 北京 100084;
2. 国网北京市电力公司电力科学研究院, 北京 100075

收稿日期:2018-07-05 修回日期:2019-02-13 出版日期:2019-04-05 发布日期:2019-04-16
作者简介:郭艳飞(1986-),男,硕士,工程师,从事电力系统规划与负荷预测研究,E-mail:dongfang0405@163.com;程林(1973-),男,博士,副教授,博士生导师,从事电力系统可靠性分析、电力系统规划研究,E-mail:chenglin@mail.tsinghua.edu.cn;李洪涛(1975-),男,硕士,高级工程师(教授级),从事电力系统自动化和电网技术研究,E-mail:li.hongtao@163.com;饶强(1983-),男,硕士,工程师,从事电力系统自动化和电力电子技术研究,E-mail:raoqiang@bj.sgcc.com.cn;刘满君(1987-),男,博士,工程师,从事电力系统可靠性分析和电力系统规划研究,E-mail:liumanjun@mail.tsinghua.edu.cn
基金资助:
国家自然科学基金资助项目（51777105）；国家电网公司总部科技项目（5202011600U2）。

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

GUO Yanfei¹, CHENG Lin¹, LI Hongtao², RAO Qiang², LIU Manjun¹

1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;
2. Beijing Electric Power Research Institute, State Grid Beijing Electric Power Company, Beijing 100075, China

Received:2018-07-05 Revised:2019-02-13 Online:2019-04-05 Published:2019-04-16
Supported by:
This work is supported by National Natural Science Foundation of China (No. 51777105), Science and Technology Project of State Grid Corporation of China (No.5202011600U2).

摘要/Abstract

摘要： 为提高电网规划阶段的空间负荷预测精度，提出了一种基于支持向量机和互联网信息修正的空间负荷预测（spatial load forecasting，SLF）方法，该方法分为3个步骤：一是基于k-均值聚类分析和支持向量回归模型得到地块负荷初始预测值；二是基于地块负荷历史数据计算负荷实际值与初始预测值之间的偏差；三是针对这些偏差，利用搜索引擎获取互联网信息，识别造成偏差的不确定事件，包括元胞中新增大负荷事件和元胞中企业营收增长率突变事件。定性分析事件对空间负荷的影响，并建立这两类事件与其造成的影响之间的分类事件影响定量模型，基于该模型对地块负荷初始预测值进行修正，得到规划区域内的地块负荷预测值。通过对北京某地区进行算例验证，结果表明该方法可以提高预测精度，可用于配电网以及能源互联网规划中的空间负荷预测。

关键词: 电力系统分析, 空间负预测, k-均值聚类, 支持向量机, 互联网信息

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

中图分类号:

TM715

郭艳飞, 程林, 李洪涛, 饶强, 刘满君. 基于支持向量机和互联网信息修正的空间负荷预测方法[J]. 中国电力, 2019, 52(4): 80-88.

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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基于支持向量机和互联网信息修正的空间负荷预测方法

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

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基于支持向量机和互联网信息修正的空间负荷预测方法

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

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