Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer

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....

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Bibliographic Details
Main Authors: Mohd Herwan, Sulaiman, Zuriani, Mustaffa
Format: Article
Language:English
Published: Elsevier B.V. 2025
Subjects:
QA75 Electronic computers. Computer science
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
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

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