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
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(deep learning，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。
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.
标题：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
title: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
authors:Yu FENG, Youbin SONG, Sheng JIN, Jiahuan FENG, Xuechen SHI, Yongjie YU, Xianchao HUANG
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),
keywords:power load forecasting,random forest (RF) algorithm,deep learning (DL),rough set theory (RST),
发表日期：2023-12-31
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