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 2025-07-01
Series:Next Energy
Subjects:
Barnacles mating optimizer
Chiller power consumption forecasting
Convolution neural networks
Long short-term memory
Metaheuristic algorithms
Online Access:http://www.sciencedirect.com/science/article/pii/S2949821X25000845
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http://www.sciencedirect.com/science/article/pii/S2949821X25000845

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