电力信息与通信技术2026，Vol.24Issue(1)：23-33,中插1,12.DOI:10.16543/j.2095-641x.electric.power.ict.2026.01.03
基于CPC-KWS算法和混合分解方法的短期电力负荷预测
Short-term Power Load Forecasting Based on DPC-KWS Algorithm and Hybrid Decomposition Method
摘要
Abstract
Power load forecasting is one of the key tasks in stable operation and rational planning of power system,which involves the accurate estimation of power demand in a certain time in the future.In order to improve the accuracy of power load forecasting,a short-term load forecasting method based on the algorithm of density peaks clustering with K-nearest neighbors and weighted similarity(DPC-KWS)and hybrid decomposition method is proposed.Firstly,the DPC-KWS algorithm is used to cluster the load data with the same power consumption behavior into one class.Secondly,the load series after clustering are decomposed into trend component and daily fluctuation component by ensemble patch transform(EPT).Then,improve complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)decomposed into components of different frequencies;Finally,the trend component and the components of different frequencies are extracted from the sequence by temporal convolutional networks(TCN)for short-term features,and then the long-term dependencies in the data are captured and predicted by the long short-term memory network(LSTM).Finally,the forecast results are reconstructed to get the final load forecast results.The comparison with the experimental results of existing models shows that the proposed method is accurate in load forecasting,which verifies the accuracy of the proposed method.关键词
负荷预测/混合分解/聚类算法/时间卷积网络/长短期记忆网络Key words
load forecasting/hybrid decomposition/clustering algorithm/temporal convolutional networks/long short-term memory network分类
信息技术与安全科学引用本文复制引用
侯佳龙,张钊,周红艳,陈雪波..基于CPC-KWS算法和混合分解方法的短期电力负荷预测[J].电力信息与通信技术,2026,24(1):23-33,中插1,12.基金项目
辽宁省高校基本科研业务费专项资金项目(LJ212410146025). （LJ212410146025）
