Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

• Published in: Results in engineering Vol. 22; p. 102148
• Main Authors: Boutahri, Youssef, Tilioua, Amine
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024; Elsevier
• Subjects: Energy efficiency; HVAC systems; Machine learning; Model predictive control; Smart building; Thermal comfort; Model predictive control; Thermal comfort; Energy efficiency; Smart building; HVAC systems; Machine learning
• DOI: 10.1016/j.rineng.2024.102148
• ISSN: 2590-1230

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. •A predictive model leveraging ML methodologies has been devised.•The objective entails forecasting thermal comfort levels and mitigating energy utilization within HVAC frameworks.•Four distinct algorithms, namely SVM, ANN, RF, and XGBOOST, are employed.•The efficacy of ML algorithms is assessed through RMSE, MSE, MAE, and R2.•Notably, RF and XGBOOST algorithms exhibit superior performance, attaining accuracies of 0.967 and 0.964, respectively.