发电技术2023，Vol.44Issue(6)：889-895,7.DOI:10.12096/j.2096-4528.pgt.23013
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
摘要
Abstract
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)Key words
power load forecasting/random forest(RF)algorithm/deep learning(DL)/rough set theory(RST)分类
能源科技引用本文复制引用
封钰,宋佑斌,金晟,冯家欢,史雪晨,俞永杰,黄弦超..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[J].发电技术,2023,44(6):889-895,7.基金项目
内蒙古自治区"揭榜挂帅"科技项目(2022JBGS0043). Project Supported by Inner Mongolia Autonomous Region"Take the Lead"Science and Technology Project(2022JBGS0043). （2022JBGS0043）
