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
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