Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer
This paper addresses the critical challenge of energy efficiency in commercial buildings, where chillers typically consume 40–50% of total building energy. Accurate forecasting of chiller power consumption is essential for optimizing building energy management systems and reducing operational costs....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44812/ http://umpir.ump.edu.my/id/eprint/44812/1/Chiller%20power%20consumption%20forecasting%20for%20commercial%20building.pdf |