A hybrid power load forecasting model using BiStacking and TCN-GRU

Open Access

Peer-reviewed

Research Article

About the Authors

Jun Ma

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Affiliation Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China

Jishen Peng

Roles Conceptualization

Affiliation Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China

Haotong Han

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

Affiliation Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China

Liye Song

Roles Conceptualization

Affiliation Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China

Hao Liu

Roles Investigation, Writing – review & editing

Affiliation Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China

The authors have declared that no competing interests exist.

Forecasting
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Deep learning
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Machine learning
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Machine learning algorithms
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Convolution
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Electricity
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Power grids
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Data processing
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