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Applied Mathematics and Nonlinear Sciences
Volumen 9 (2024): Edición 1 (Enero 2024)
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Short-term power load forecasting model based on multi-strategy improved WOA optimized LSTM
Qian Liang
Qian Liang
Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology
Guilin, China
College of Mechanical and Control Engineering, Guilin University of Technology
Guilin, China
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Liang, Qian
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Wencheng Wang
Wencheng Wang
Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology
Guilin, China
College of Mechanical and Control Engineering, Guilin University of Technology
Guilin, China
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Wang, Wencheng
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Yinchao Wang
Yinchao Wang
Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology
Guilin, China
College of Mechanical and Control Engineering, Guilin University of Technology
Guilin, China
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Wang, Yinchao
26 feb 2024
Applied Mathematics and Nonlinear Sciences
Volumen 9 (2024): Edición 1 (Enero 2024)
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Publicado en línea:
26 feb 2024
Recibido:
02 ene 2024
Aceptado:
09 ene 2024
DOI:
https://doi.org/10.2478/amns-2024-0323
Palabras clave
<kwd>Short-term electric load forecasting</kwd>
,
<kwd>Long and short-term memory networks</kwd>
,
<kwd>Whale optimization algorithm</kwd>
,
<kwd>Machine learning</kwd>
,
<kwd>Adaptive Weights</kwd>
© 2024 Qian Liang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
