为有效提高短期电力负荷预测精度，提出了一种基于重构分解和误差补偿的双层协同预测方法。上层以改进的完全集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise
ICEEMDAN)）、排列熵、K-medoids聚类和自适应变分模态分解组成的重构分解方法降低负荷序列的不可预测性，进一步构建时间卷积网络联立双向门控循环单元的混合预测模型（temporalconvolutional network-bidirectional gated recurrent unit
González Grandón T.,Schwenzer J.,Steens T.,Breuing J..Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine[J].Applied Energy,2024.
Mounir Nada,Ouadi Hamid,Jrhilifa Ismael.Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system[J].Energy & Buildings,2023.
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