Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
Building energy efficiency is crucial for global sustainability efforts, with chillers representing major energy consumers in commercial buildings. Accurate prediction of chiller power consumption remains challenging due to complex operational parameters, with feature selection being critical for mo...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier LTD
2025
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| 主題: | |
| オンライン･アクセス: | http://umpir.ump.edu.my/id/eprint/44263/1/Feature%20Optimization%20with%20Metaheuristics%20for%20Artificial%20Neural%20Network-based%20Chiller%20Power%20Prediction.pdf http://umpir.ump.edu.my/id/eprint/44263/ https://doi.org/10.1016/j.jobe.2025.112561 https://doi.org/10.1016/j.jobe.2025.112561 |
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http://umpir.ump.edu.my/id/eprint/44263/1/Feature%20Optimization%20with%20Metaheuristics%20for%20Artificial%20Neural%20Network-based%20Chiller%20Power%20Prediction.pdfhttp://umpir.ump.edu.my/id/eprint/44263/
https://doi.org/10.1016/j.jobe.2025.112561
https://doi.org/10.1016/j.jobe.2025.112561