短期负荷预测中气象因素处理的费歇信息方法  

Fisher information method for processing weather factors in short-term load forecasting

作　　者：蔡舒平[1] 张保会[2] 汤大海 陈燕 郝治国[2] CAI Shuping;ZHANG Baohui;TANG Dahai;CHEN Yan;HAO Zhiguo(College of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China;School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China;Zhenjiang Power Supply Company of State Grid Jiangsu Electric Power Co.,Ltd.,Zhenjiang 212001,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013 [2]西安交通大学电气工程学院,陕西西安710049 [3]国网江苏省电力有限公司镇江供电分公司,江苏镇江212001

出　　处：《电力自动化设备》2020年第3期141-146,共6页Electric Power Automation Equipment

基　　金：国家自然科学基金资助项目(61301138,51975260);江苏高校优势学科建设工程资助课题(批准号:苏政办发(2011)6)。

摘　　要：在能提供大量实时负荷数据和气象数据的智能电网大数据环境下,挖掘合适的气象因素处理方法对提高短期负荷预测精度尤为重要。针对一个或多个气象变量,解决一维或多维费歇信息计算问题。在此基础上,提出基于费歇信息的气象因素建模方法及新预测模型。实际测试结果表明:采用所提模型可以获得更精确的预测结果,解决了短期负荷预测中对气象因素处理的主观随意性问题。In the big data environment of smart grid that can afford massive real-time load data and weather data,it is especially important to find appropriate weather factor processing method for improving the accuracy of short-term load forecasting. Aiming at one or multiple weather variables,the problems of one or multi-dimension Fisher information calculation are solved. On this basis,the modeling method of weather factors based on Fisher information and the new forecasting model are proposed. The practical test results show that the proposed model can obtain more accurate forecasting results,which solves the subjective randomness problem in dealing with weather factors of short-term load forecasting.

关 键 词：智能电网 短期负荷预测 气象因素 费歇信息 累积效应 模型 

分 类 号：TM714[电气工程—电力系统及自动化]