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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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Main Authors:	Nor Farizan, Zakaria, Mohd Herwan, Sulaiman, Zuriani, Mustaffa
格式:	Article
语言:	English
出版:	Elsevier LTD 2025
主题:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
在线阅读:	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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id	my.ump.umpir.44263
record_format	eprints
spelling	my.ump.umpir.442632025-04-08T01:27:46Z http://umpir.ump.edu.my/id/eprint/44263/ Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction Nor Farizan, Zakaria Mohd Herwan, Sulaiman Zuriani, Mustaffa QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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 model performance. This study aims to develop an improved feature selection approach that enhances prediction accuracy while reducing computational complexity in chiller consumption forecasting. This paper presents a novel Evolutionary Mating Algorithm (EMA) hybridized with Artificial Neural Networks (ANN) for optimizing feature selection. The EMA-ANN approach was compared against other metaheuristic-ANN hybrid models using operational data from a commercial building's chiller system. EMA-ANN demonstrated superior prediction accuracy with the lowest Mean Absolute Error (0.2235), Root Mean Square Error (0.4150), and highest coefficient of determination (R² = 0.9689). The algorithm identified seven optimal features primarily comprising temperature and humidity parameters. The algorithm’s unique evolutionary mating mechanism with adaptive crossover rate (Cr = 0.85), enabled effective feature space exploration, resulting in a 38.3% reduction in RMSE and 6.0% improvement in R2 compared to models without feature selection. This research contributes a novel hybrid model, identifies key features for chiller power prediction, and establishes a benchmark for evaluating feature selection algorithms in building energy applications. Elsevier LTD 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/44263/1/Feature%20Optimization%20with%20Metaheuristics%20for%20Artificial%20Neural%20Network-based%20Chiller%20Power%20Prediction.pdf Nor Farizan, Zakaria and Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2025) Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction. Journal of Building Engineering, 105 (112561). pp. 1-18. ISSN 2352-7102. (Published) https://doi.org/10.1016/j.jobe.2025.112561 https://doi.org/10.1016/j.jobe.2025.112561
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Nor Farizan, Zakaria Mohd Herwan, Sulaiman Zuriani, Mustaffa Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
description	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 model performance. This study aims to develop an improved feature selection approach that enhances prediction accuracy while reducing computational complexity in chiller consumption forecasting. This paper presents a novel Evolutionary Mating Algorithm (EMA) hybridized with Artificial Neural Networks (ANN) for optimizing feature selection. The EMA-ANN approach was compared against other metaheuristic-ANN hybrid models using operational data from a commercial building's chiller system. EMA-ANN demonstrated superior prediction accuracy with the lowest Mean Absolute Error (0.2235), Root Mean Square Error (0.4150), and highest coefficient of determination (R² = 0.9689). The algorithm identified seven optimal features primarily comprising temperature and humidity parameters. The algorithm’s unique evolutionary mating mechanism with adaptive crossover rate (Cr = 0.85), enabled effective feature space exploration, resulting in a 38.3% reduction in RMSE and 6.0% improvement in R2 compared to models without feature selection. This research contributes a novel hybrid model, identifies key features for chiller power prediction, and establishes a benchmark for evaluating feature selection algorithms in building energy applications.
format	Article
author	Nor Farizan, Zakaria Mohd Herwan, Sulaiman Zuriani, Mustaffa
author_facet	Nor Farizan, Zakaria Mohd Herwan, Sulaiman Zuriani, Mustaffa
author_sort	Nor Farizan, Zakaria
title	Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
title_short	Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
title_full	Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
title_fullStr	Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
title_full_unstemmed	Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction
title_sort	feature optimization with metaheuristics for artificial neural network-based chiller power prediction
publisher	Elsevier LTD
publishDate	2025
url	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
_version_	1828878997646213120
score	13.251813

Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction

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