基于随机森林算法和粗糙集理论的改进型深度学习短期负荷预测模型  

Improved Deep Learning Model for Forecasting Short-Term Load Based on Random Forest Algorithm and Rough Set Theory

作　　者：封钰 宋佑斌 金晟 冯家欢 史雪晨 俞永杰 黄弦超[3] FENG Yu;SONG Youbin;JIN Sheng;FENG Jiahuan;SHI Xuechen;YU Yongjie;HUANG Xianchao(Suzhou Power Supply Branch,State Grid Jiangsu Electric Power Co.,Ltd.,Suzhou 215004,Jiangsu Province,China;Qiantang District Power Supply Branch,State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou 310000,Zhejiang Province,China;School of Electrical and Electronic Engineering,North China Electric Power University,Changping District,Beijing 102206,China)

机构地区：[1]国网江苏省电力有限公司苏州供电分公司,江苏省苏州市215004 [2]国网浙江省电力有限公司杭州市钱塘区供电公司,浙江省杭州市310000 [3]华北电力大学电气与电子工程学院,北京市昌平区102206

出　　处：《发电技术》2023年第6期889-895,共7页Power Generation Technology

基　　金：内蒙古自治区“揭榜挂帅”科技项目(2022JBGS0043)。

摘　　要：精准的电力负荷预测有利于保障电力系统的安全、经济运行。针对现行预测算法存在的预测准确度低、模型耗时长等问题,提出一种基于随机森林(random forest,RF)算法和粗糙集理论(rough set theory,RST)的改进型深度学习(deeplearning, DL)短期负荷预测模型(RF-DL-RST)。该模型首先基于历史数据,利用随机森林算法提取影响负荷预测的关键特征量;然后将关键特征量和历史负荷值作为深度神经网络的输入、输出项进行训练,并通过粗糙集理论修正预测结果。最后,通过算例进行仿真验证,结果表明,该模型的预测准确度比单一的深度学习模型及不进行预测修正的模型更高。Accurate power load forecasting is conducive to ensuring the safe and economic operation of the power system.Aiming at the problems of low prediction accuracy and long time consuming of the current prediction algorithms,an improved deep learning(DL)short-term load forecasting model based on random forest(RF)algorithm and rough set theory(RST),namely RF-DL-RST,was proposed.Firstly,based on historical data,the model used RF algorithm to extract the key features that affected the load forecasting.Then,the key features and historical load data were trained as the input and output items of deep neural network(DNN),and the prediction results were corrected by RST.After that,the rough set method was used to revise the prediction results.Finally,the simulation was verified by an example.The results show that the prediction accuracy of the model is higher than that of a single DNN model and a model without RST revised.

关 键 词：电力负荷预测 随机森林(RF)算法 深度学习(DL) 粗糙集理论(RST) 

分 类 号：TK01[动力工程及工程热物理]