A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model
Abstract Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87776-0 |
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| author | Meng He Hui Wang Myo Thwin |
| author_facet | Meng He Hui Wang Myo Thwin |
| author_sort | Meng He |
| collection | DOAJ |
| description | Abstract Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems. The model is based on using an optimized design of Gated recurrent unit (GRU) network and an enhanced metaheuristic algorithm, named Improved Alpine Skiing Optimizer (IASO). GRU is a recurrent neural network that has the ability to comprehend intricate temporal relationships within the input data. On the other hand, the IASO technique has been considered to be a population-based optimization technique emulating the downhill skiing behavior of skiers. The proposed GRU/IASO model is trained and tested utilizing data of real-world obtained through a commercial complex situated within an area characterized by high humidity and hot climate. By comparing the proposed method with some other commonly used techniques, including ---, the advantage of the suggested model regarding accuracy and robustness has been defined. |
| format | Article |
| id | doaj-art-0e65b18b91924acc9493029cfaa81c3d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0e65b18b91924acc9493029cfaa81c3d2025-02-02T12:22:50ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-87776-0A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO modelMeng He0Hui Wang1Myo Thwin2School of Software and Big Data, Changzhou College of Information TechnologySchool of Internet of Things Engineering, Jiangsu Vocational College of Information TechnologyYangon Technological UniversityAbstract Air conditioning systems are widely used to provide thermal comfort in hot and humid regions, but they also consume a large amount of energy. Therefore, accurate and reliable load demand forecasting is essential for energy management and optimization in air conditioning systems. Within the current paper, a novel model on the basis of machine learning has been presented for dynamic optimal load demand forecasting in air conditioning systems. The model is based on using an optimized design of Gated recurrent unit (GRU) network and an enhanced metaheuristic algorithm, named Improved Alpine Skiing Optimizer (IASO). GRU is a recurrent neural network that has the ability to comprehend intricate temporal relationships within the input data. On the other hand, the IASO technique has been considered to be a population-based optimization technique emulating the downhill skiing behavior of skiers. The proposed GRU/IASO model is trained and tested utilizing data of real-world obtained through a commercial complex situated within an area characterized by high humidity and hot climate. By comparing the proposed method with some other commonly used techniques, including ---, the advantage of the suggested model regarding accuracy and robustness has been defined.https://doi.org/10.1038/s41598-025-87776-0Load demand forecastingAir conditioning systemsGated recurrent unitImproved Alpine Skiing optimizationMachine learning |
| spellingShingle | Meng He Hui Wang Myo Thwin A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model Scientific Reports Load demand forecasting Air conditioning systems Gated recurrent unit Improved Alpine Skiing optimization Machine learning |
| title | A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model |
| title_full | A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model |
| title_fullStr | A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model |
| title_full_unstemmed | A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model |
| title_short | A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model |
| title_sort | machine learning technique for optimizing load demand prediction within air conditioning systems utilizing gru iaso model |
| topic | Load demand forecasting Air conditioning systems Gated recurrent unit Improved Alpine Skiing optimization Machine learning |
| url | https://doi.org/10.1038/s41598-025-87776-0 |
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