Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings
In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302400402X |
| _version_ | 1797198812383543296 |
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| author | Youssef Boutahri Amine Tilioua |
| author_facet | Youssef Boutahri Amine Tilioua |
| author_sort | Youssef Boutahri |
| collection | DOAJ |
| description | In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation. |
| first_indexed | 2024-04-24T07:05:48Z |
| format | Article |
| id | doaj.art-cdb0678795f740d6a28e45a6fb86248d |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T07:05:48Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-cdb0678795f740d6a28e45a6fb86248d2024-04-22T04:11:55ZengElsevierResults in Engineering2590-12302024-06-0122102148Machine learning-based predictive model for thermal comfort and energy optimization in smart buildingsYoussef Boutahri0Amine Tilioua1Corresponding author.; Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.). Department of Engineering Sciences, Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl University of Meknès, B.P. 509, Boutalamine, Errachidia, MoroccoCorresponding author.; Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.). Department of Engineering Sciences, Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl University of Meknès, B.P. 509, Boutalamine, Errachidia, MoroccoIn the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.http://www.sciencedirect.com/science/article/pii/S259012302400402XThermal comfortEnergy efficiencyHVAC systemsMachine learningModel predictive controlSmart building |
| spellingShingle | Youssef Boutahri Amine Tilioua Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings Results in Engineering Thermal comfort Energy efficiency HVAC systems Machine learning Model predictive control Smart building |
| title | Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings |
| title_full | Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings |
| title_fullStr | Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings |
| title_full_unstemmed | Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings |
| title_short | Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings |
| title_sort | machine learning based predictive model for thermal comfort and energy optimization in smart buildings |
| topic | Thermal comfort Energy efficiency HVAC systems Machine learning Model predictive control Smart building |
| url | http://www.sciencedirect.com/science/article/pii/S259012302400402X |
| work_keys_str_mv | AT youssefboutahri machinelearningbasedpredictivemodelforthermalcomfortandenergyoptimizationinsmartbuildings AT aminetilioua machinelearningbasedpredictivemodelforthermalcomfortandenergyoptimizationinsmartbuildings |