气候变化领域集成服务门户(CCPortal): Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

CCPortal

DOI	10.1016/j.rineng.2024.102148
	Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings
	Boutahri, Youssef; Tilioua, Amine
发表日期	2024
ISSN	2590-1230
起始页码	22
卷号	22
英文摘要	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.
英文关键词	Thermal comfort; Energy efficiency; HVAC systems; Machine learning; Model predictive control; Smart building
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Multidisciplinary
WOS记录号	WOS:001234603900002
来源期刊	RESULTS IN ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296655
作者单位	Moulay Ismail University of Meknes
推荐引用方式 GB/T 7714	Boutahri, Youssef,Tilioua, Amine. Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings[J],2024,22.
APA	Boutahri, Youssef,&Tilioua, Amine.(2024).Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings.RESULTS IN ENGINEERING,22.
MLA	Boutahri, Youssef,et al."Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings".RESULTS IN ENGINEERING 22(2024).