内蒙古电力技术2024，Vol.42Issue(6)：56-63,8.DOI:10.19929/j.cnki.nmgdljs.2024.0080
基于AHP-K-Means-LSTM模型的短期电力负荷预测研究
Research on Short Term Power Load Forecasting Based on AHP-K-Means-LSTM Model
摘要
Abstract
In order to further improve the prediction performance,this paper proposes a short-term load forecasting method using the AHP-K-Means-LSTM combination model from the dimensions of weight and clustering.Firstly,the Analytic Hierarchy Process(AHP)is used to calculate the weights of factors that affect load forecasting.The improved K-Means clustering algorithm is combined to select the most effective clustering results from the samples.Then,the sample is brought into the Long Short-Term Memory(LSTM)neural network model for training,and the output results are compared and analyzed with the actual load.Taking the 2022 electricity load dataset in Shenyang of Liaoning Province as an example for simulation verification,the results of which show that the proposed method has improved load forecasting accuracy compared to traditional methods in working days and holidays in different seasons.关键词
短期负荷预测/大数据分析/层次分析法/K-Means聚类/LSTM神经网络Key words
short term load forecasting/big data analysis/Analytic Hierarchy Process/K-Means clustering/LSTM neural network分类
动力与电气工程引用本文复制引用
章家栋,张永庆,陈修鹏,单偶双,张巍巍..基于AHP-K-Means-LSTM模型的短期电力负荷预测研究[J].内蒙古电力技术,2024,42(6):56-63,8.基金项目
国网辽宁省电力有限公司科技项目"基于大数据分析的用电负荷结构特征及稳定性态势感知技术研究"(2024YF-02) （2024YF-02）
