机械与电子2026，Vol.44Issue(8)：117-126,10.
基于PCC提取负荷周期特征与时变趋势特征的精准短期负荷预测
Accurate Short-term Load Forecasting Based on PCC Extracted Load Cyclic Characteristics and Time-varying Trend Characteristics
摘要
Abstract
To enhance the forecasting accuracy and generalization capability of prediction models,this paper proposes a hybrid forecasting model(C-T-CNN-LSTM)that integrates load cyclic characteristics and time-varying trend characteristics into a convolutional neural network and long short-term memory(CNN-LSTM)network.Firstly,the Pearson correlation coefficient(PCC)is employed to quantify the correlation strength among load sequences at different sampling instants,thereby selecting key feature vari-ables that can reflect the inherent periodic evolution patterns of electric power load.Subsequently,based on an analysis of the temporal variation characteristics of periodic load data,the concept of load time-varying trend is introduced,and a corresponding model is established to extract the time-varying trend features.Finally,the load cyclic feature variables and the time-varying trend feature variables are reconstructed to form a feature set,which is then fed into the CNN-LSTM hybrid model for prediction.Taking the 2015 e-lectric load dataset of the New England region of the United States as a case study,nine prediction models are employed for simulation validation and comparative analysis.The results demonstrate that the proposed C-T-CNN-LSTM model achieves the highest prediction accuracy among all compared models.关键词
时变趋势特征提取/皮尔逊相关系数/CNN-LSTM混合神经网络/短期负荷预测Key words
time-varying trend feature extraction/Pearson correlation coefficient/CNN-LSTM hybrid neural network/short-term load forecasting分类
信息技术与安全科学引用本文复制引用
贾燕峰,俞飞,李飚,严亚帮,乔澳雪,季玉琦..基于PCC提取负荷周期特征与时变趋势特征的精准短期负荷预测[J].机械与电子,2026,44(8):117-126,10.基金项目
国网河南省电力公司科技项目资助(5217I0250004) （5217I0250004）
