基于Fisher信息和在线SVR的智能电网气象敏感负荷预测动态建模技术  

A Dynamic Modeling Methodology Based on Fisher Information and On-line SVR for Smart Grids Weather Sensitive Load Forecasting

作　　者：蔡舒平[1] 闫静 刘国海[1] 汤大海 陈燕 刘琳 周梓樾 CAI Shuping;YAN Jing;LIU Guohai;TANG Dahai;CHEN Yan;LIU Lin;ZHOU Ziyue(School of Electrical Information and Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu Province,China;Zhenjiang Power Supply Company of State Grid Jiangsu Electric Power Co.,Ltd,Zhenjiang 212001,Jiangsu Province,China)

机构地区：[1]江苏大学电气信息工程学院,江苏省镇江市212013 [2]国网江苏省电力有限公司镇江供电分公司,江苏省镇江市212001

出　　处：《中国电机工程学报》2020年第11期3441-3451,共11页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51577084,51877098);江苏高校优势学科建设工程(批准号:苏政办发(2011)6号)资助课题。

摘　　要：智能电网大数据环境为解决短期负荷预测模型性能退化和精度随时间降低等问题提供了契机。基于此,该文提出一种基于在线支持向量回归(on-line support vector regression,OSVR)和Fisher信息(Fisher information,FI)气象因素处理及特征选择(features selection,FS)的动态建模新方法,用该方法来构建过程变量之间关系快速变化时的智能电网气象敏感负荷预测模型。首先,利用支持向量回归(support vector regression,SVR)模型的卡罗需–库恩–塔克(Karush-Kuhn-Tucker,KKT)条件推导出一种简洁的OSVR学习算法,使得每当有样本增加到训练集或从训练集移除时,该算法均能有效地更新已训练好的SVR模型,而不用对整个训练数据重新再训练。其次,提出一种基于Fisher信息的特征选择方法和气象因素引入方法,能够从捕获的数据中提取主要特征,并有效处理气象因素的累积效应。实际测试结果表明:所建立的预测模型能够使用最新的数据信息完成更新,在过程特征发生快速变化的情况下,其预测精度仍高于传统方法。The smart grid big data environment provides an opportunity to solve the problems of short-term load forecasting model performance degradation and accuracy degradation over time.In light of this,a novel dynamic modeling methodology combining on-line SVR(OSVR)with Fisher information(FI)-based meteorological factors introduction and features selection(FS)was developed,and constructs a smart grid weather sensitive load forecasting model when the relationship between process variables changes rapidly.First of all,a concise OSVR learning algorithm was derived using Karush-Kuhn-Tucker(KKT)conditions in SVR.The OSVR algorithm efficiently updated a trained SVR function whenever a sample was added to or removed from the training set without retraining the entire training data.Secondly,a novel method based on Fisher information for meteorological variables introduction and feature selection in STLF was presented.By means of the approach,the main features can be extracted from the captured data and the cumulative effects of meteorological factors on STLF can be handled properly.The STLF model constructed by the methodology can always be updated with the latest data.When it was applied to real load data and the meteorological data,the predictive accuracy is still higher than traditional one even when the process features change rapidly.

关 键 词：动态建模 气象敏感负荷预测 特征选择 在线支持向量回归 Fisher信息 累积效应 

分 类 号：TM744[电气工程—电力系统及自动化]