电力系统保护与控制2025，Vol.53Issue(19)：114-126,13.DOI:10.19783/j.cnki.pspc.241360
基于多尺度二次特征提取的短期电力负荷预测模型
A short-term electric load forecasting model based on multi-scale secondary feature extraction
摘要
Abstract
To fully explore the complex temporal relationships among the inherent multi-scale features(MSF)of electrical load data and further improve the performance of electricity load forecasting models,especially their accuracy during holidays,a short-term electricity load forecasting model based on multi-scale secondary feature extraction is proposed.First,the Prophet algorithm is used to decompose and fit the load data,extracting components at different scales,which are then combined with correlated weather data to construct a multivariant dataset.Then,an improved feature pyramid network(IFPN)is employed to match the multi-scale characteristics of load data.A convolutional feature enhancement module is designed to strengthen the model's ability to express holiday-specific features,achieving the first extraction of MSF.Leveraging the advantages of temporal convolutional neural networks,the model deeply mines the temporal dependencies among the primary features.Squeeze-and-excitation networks(SENet)is introduced to adaptively assign weights to features,completing the secondary extraction of MSF.Finally,load forecasting is performed using a Transformer model optimized by the Osprey algorithm.Validation on two domestic and international load datasets shows that the proposed model outperforms comparison models,particularly in improving prediction accuracy during holidays.关键词
短期电力负荷预测/Prophet算法/二次特征提取/改进的特征金字塔网络/多尺度时间卷积网络Key words
short term power load forecasting/Prophet algorithm/secondary feature extraction/IFPN/MSTCN引用本文复制引用
李楠,金淳熙,陶亮,黄亮..基于多尺度二次特征提取的短期电力负荷预测模型[J].电力系统保护与控制,2025,53(19):114-126,13.基金项目
This work is supported by the General Program of National Natural Science Foundation of China(No.52277084). 国家自然科学基金面上项目资助(52277084) （No.52277084）
