Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques
Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent resid...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361803/full |
| _version_ | 1797321011897565184 |
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| author | Mengyuan Lin Liyuan Peng Tingting Liu Lili Zhang |
| author_facet | Mengyuan Lin Liyuan Peng Tingting Liu Lili Zhang |
| author_sort | Mengyuan Lin |
| collection | DOAJ |
| description | Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process. |
| first_indexed | 2024-03-08T04:51:22Z |
| format | Article |
| id | doaj.art-2529529edce8422d9e990208bd2891df |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T04:51:22Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-2529529edce8422d9e990208bd2891df2024-02-08T04:23:27ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-02-011210.3389/fenrg.2024.13618031361803Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniquesMengyuan LinLiyuan PengTingting LiuLili ZhangBuildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361803/fullbuilding energy consumptionsmart homemetaheuristic methodsdata miningsupport vector machinerandom forest |
| spellingShingle | Mengyuan Lin Liyuan Peng Tingting Liu Lili Zhang Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques Frontiers in Energy Research building energy consumption smart home metaheuristic methods data mining support vector machine random forest |
| title | Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques |
| title_full | Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques |
| title_fullStr | Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques |
| title_full_unstemmed | Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques |
| title_short | Novel approach to energy consumption estimation in smart homes: application of data mining and optimization techniques |
| title_sort | novel approach to energy consumption estimation in smart homes application of data mining and optimization techniques |
| topic | building energy consumption smart home metaheuristic methods data mining support vector machine random forest |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1361803/full |
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