has been cited by the following article(s):
[1]
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The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA
IEEE Access,
2024
DOI:10.1109/ACCESS.2024.3377097
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[2]
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Artificial Intelligence Techniques for Load Forecasting in an Electric Utility
2024 IEEE International Conference on Electro Information Technology (eIT),
2024
DOI:10.1109/eIT60633.2024.10609930
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[3]
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Deep Learning Evolved: Overcoming Sub-Optimal Local Minima with $(\mu/\rho+\lambda)$–Evolution Strategies
2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE),
2023
DOI:10.1109/CSCE60160.2023.00012
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[4]
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Deep Learning Evolved: Overcoming Sub-Optimal Local Minima with $(\mu/\rho+\lambda)$–Evolution Strategies
2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE),
2023
DOI:10.1109/CSCE60160.2023.00012
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[5]
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Intelligent Systems for Power Load Forecasting: A Study Review
Energies,
2020
DOI:10.3390/en13226105
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[6]
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Intelligent Systems for Power Load Forecasting: A Study Review
Energies,
2020
DOI:10.3390/en13226105
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[7]
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Analysis of correlation between meteorological factors and short-term load forecasting based on machine learning
2018 International Conference on Power System Technology (POWERCON),
2018
DOI:10.1109/POWERCON.2018.8601585
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[8]
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Boosted neural networks for improved short-term electric load forecasting
Electric Power Systems Research,
2017
DOI:10.1016/j.epsr.2016.10.067
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[9]
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Improved short-term load forecasting using bagged neural networks
Electric Power Systems Research,
2015
DOI:10.1016/j.epsr.2015.03.027
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