Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building
Accurate prediction of chiller energy consumption is essential for optimizing energy usage and reducing operational costs in commercial buildings. Traditional predictive methods often struggle to capture the complex, nonlinear relationships inherent in energy consumption data. This study proposes th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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Elsevier Ltd
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42921/ http://umpir.ump.edu.my/id/eprint/42921/1/Utilizing%20the%20Kolmogorov-Arnold%20Networks_ABST.pdf http://umpir.ump.edu.my/id/eprint/42921/2/Utilizing%20the%20Kolmogorov-Arnold%20Networks.pdf |
| _version_ | 1848826735819227136 |
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| author | Mohd Herwan, Sulaiman Zuriani, Mustaffa Muhammad Salihin, Saealal Mohd Mawardi, Saari Abu Zaharin, Ahmad |
| author_facet | Mohd Herwan, Sulaiman Zuriani, Mustaffa Muhammad Salihin, Saealal Mohd Mawardi, Saari Abu Zaharin, Ahmad |
| author_sort | Mohd Herwan, Sulaiman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Accurate prediction of chiller energy consumption is essential for optimizing energy usage and reducing operational costs in commercial buildings. Traditional predictive methods often struggle to capture the complex, nonlinear relationships inherent in energy consumption data. This study proposes the use of Kolmogorov-Arnold Networks (KAN) to address this challenge, leveraging their ability to model intricate nonlinear dynamics with high precision. The study introduces KAN as a novel application for real-world chiller energy prediction, using actual data obtained from a commercial building. The methodology involves comparing KAN's performance with Artificial Neural Networks (NN) and a hybrid metaheuristic algorithm combined with deep learning, namely the Teaching-Learning-Based Optimization with Deep Learning (TLBO-DL). The results show that KAN achieves an R2 value of 0.9465 and an RMSE of 6.1023, outperforming NN (R2: 0.9281, RMSE: 6.7709) and TLBO-DL (R2: 0.9366, RMSE: 6.2892). The novelty of this research lies in the innovative application of KAN to chiller energy consumption prediction, coupled with advanced parameter tuning and improved computational efficiency. This study not only demonstrates the superior accuracy of KAN but also contributes to the field by showcasing its practical utility and effectiveness in energy management systems. |
| first_indexed | 2025-11-15T03:49:33Z |
| format | Article |
| id | ump-42921 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:49:33Z |
| publishDate | 2024 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-429212024-11-13T06:56:19Z http://umpir.ump.edu.my/id/eprint/42921/ Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building Mohd Herwan, Sulaiman Zuriani, Mustaffa Muhammad Salihin, Saealal Mohd Mawardi, Saari Abu Zaharin, Ahmad TK Electrical engineering. Electronics Nuclear engineering Accurate prediction of chiller energy consumption is essential for optimizing energy usage and reducing operational costs in commercial buildings. Traditional predictive methods often struggle to capture the complex, nonlinear relationships inherent in energy consumption data. This study proposes the use of Kolmogorov-Arnold Networks (KAN) to address this challenge, leveraging their ability to model intricate nonlinear dynamics with high precision. The study introduces KAN as a novel application for real-world chiller energy prediction, using actual data obtained from a commercial building. The methodology involves comparing KAN's performance with Artificial Neural Networks (NN) and a hybrid metaheuristic algorithm combined with deep learning, namely the Teaching-Learning-Based Optimization with Deep Learning (TLBO-DL). The results show that KAN achieves an R2 value of 0.9465 and an RMSE of 6.1023, outperforming NN (R2: 0.9281, RMSE: 6.7709) and TLBO-DL (R2: 0.9366, RMSE: 6.2892). The novelty of this research lies in the innovative application of KAN to chiller energy consumption prediction, coupled with advanced parameter tuning and improved computational efficiency. This study not only demonstrates the superior accuracy of KAN but also contributes to the field by showcasing its practical utility and effectiveness in energy management systems. Elsevier Ltd 2024-11-01 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42921/1/Utilizing%20the%20Kolmogorov-Arnold%20Networks_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/42921/2/Utilizing%20the%20Kolmogorov-Arnold%20Networks.pdf Mohd Herwan, Sulaiman and Zuriani, Mustaffa and Muhammad Salihin, Saealal and Mohd Mawardi, Saari and Abu Zaharin, Ahmad (2024) Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building. Journal of Building Engineering, 96 (110475). pp. 1-16. ISSN 2352-7102. (Published) https://doi.org/10.1016/j.jobe.2024.110475 https://doi.org/10.1016/j.jobe.2024.110475 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Herwan, Sulaiman Zuriani, Mustaffa Muhammad Salihin, Saealal Mohd Mawardi, Saari Abu Zaharin, Ahmad Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building |
| title | Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building |
| title_full | Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building |
| title_fullStr | Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building |
| title_full_unstemmed | Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building |
| title_short | Utilizing the Kolmogorov-Arnold Networks for chiller energy consumption prediction in commercial building |
| title_sort | utilizing the kolmogorov-arnold networks for chiller energy consumption prediction in commercial building |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/42921/ http://umpir.ump.edu.my/id/eprint/42921/ http://umpir.ump.edu.my/id/eprint/42921/ http://umpir.ump.edu.my/id/eprint/42921/1/Utilizing%20the%20Kolmogorov-Arnold%20Networks_ABST.pdf http://umpir.ump.edu.my/id/eprint/42921/2/Utilizing%20the%20Kolmogorov-Arnold%20Networks.pdf |