Short-Term Power Load Forecasting of Integrated Energy System Based on Attention-CNN-DBILSTM

Short-Term Power Load Forecasting of Integrated Energy System Based on Attention-CNN-DBILSTM

In view of the fact that the potential high-dimensional features in the historical sequence are difficult to be effectively extracted by traditional power load forecasting methods and the coupling factors of electricity, heat, and gas have not been considered, the correlation of electric heating and...

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Bibliographic Details
Main Authors:	Wang, Q. (Author), Yao, Z. (Author), Zhang, T. (Author), Zhao, Y. (Author)
Format:	Article
Language:	English
Published:	Hindawi Limited 2022
Subjects:	Convolution Coupling factor Couplings Data mining Dynamic changes Electric load forecasting Electric power plant loads Forecasting methods Gas load Higher dimensional features Integrated energy systems Long short-term memory Memory network Multiple loads Power load forecasting Prediction accuracy Time series
Online Access:	View Fulltext in Publisher

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