Chiller energy prediction in commercial building : A metaheuristic-enhanced deep learning approach

Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2024) Chiller energy prediction in commercial building : A metaheuristic-enhanced deep learning approach. Energy, 297 (131159). pp. 1-13. ISSN 0360-5442. (Published)

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Abstract

Chiller energy prediction in commercial building : A metaheuristic-Enhanced deep learning approach Chiller systems hold a critical role in upholding comfort and energy efficiency within commercial buildings. Precise prediction of chiller energy consumption is imperative for operational optimization and the reduction of energy expenditures. This paper introduces an innovative methodology that integrates deep learning (DL), specifically Fixed Forward Neural Networks (FFNN), with Teaching-Learning-Based Optimization (TLBO) to enhance the accuracy of chiller energy consumption forecasts. Drawing on a diverse dataset from a commercial building, encompassing vital input parameters such as Chilled Water Rate, Building Load, Cooling Water Temperature, Humidity, and Dew Point, the study conducts a comprehensive comparison of metaheuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Harmony Search Algorithm (HSA), Differential Evolution (DE), Ant Colony Optimization (ACO), and the latest RIME algorithm). TLBO's adept navigation of the intricate parameter space of DL yields highly precise predictions for chiller energy consumption. The study's outcomes underscore TLBO's potential, along with other metaheuristics, in optimizing DL and refining energy management practices in commercial buildings. This research significantly contributes to the evolving discourse on the symbiosis between DL, particularly FFNNs, and metaheuristic optimization, offering a robust framework for chiller energy consumption prediction, thereby advancing sustainability and cost-effectiveness in building operations.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Chiller energy consumption; Deep learning; HVAC; Hybrid algorithm; Metaheuristic algorithm
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Computing Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	05 Jun 2024 04:19
Last Modified:	05 Jun 2024 04:19
URI:	http://umpir.ump.edu.my/id/eprint/41375
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