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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| Language: | en |
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Elsevier LTD
2025
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| Online Access: | 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 |
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| author | Nor Farizan, Zakaria Mohd Herwan, Sulaiman Zuriani, Mustaffa |
| author_facet | Nor Farizan, Zakaria Mohd Herwan, Sulaiman Zuriani, Mustaffa |
| author_sort | Nor Farizan, Zakaria |
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| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.44263 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2025 |
| publisher | Elsevier LTD |
| 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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| 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 |
| url_provider | http://umpir.ump.edu.my/ |