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- W4383749002 abstract "Economic development highly relies on energy, and the causal relationship among energy demand of different sectors is closely related to the sector chain relationship. The existing researches on sectoral electricity demand lack the research on the causal relationship between sectoral electricity demand, which leads to the weak interpretation of the selected input indicators in the sectoral electricity forecast method. To this end, this paper innovatively adopts the convergence cross mapping algorithm (CCM) to identify the causal relationship of the change of electricity demand in various sectors, adaptively screens the prospective indicators that affect the sectoral electricity demand, and proposes a monthly sectoral electricity forecast method with embedded causality based on the temporal convolutional attention network (TCAN) model. This method can effectively identify the causality of electricity demand between sectors, and effectively improve the accuracy of monthly sectoral electricity demand forecast. Finally, the applicability and effectiveness of the proposed forecast method are illustrated based on the realistic data." @default.
- W4383749002 created "2023-07-11" @default.
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- W4383749002 date "2023-05-12" @default.
- W4383749002 modified "2023-10-15" @default.
- W4383749002 title "Monthly Sectoral Electricity Demand Forecast considering Sector Chain Causal Relations using CCM-TCAN" @default.
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- W4383749002 doi "https://doi.org/10.1109/cieec58067.2023.10165773" @default.
- W4383749002 hasPublicationYear "2023" @default.
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