Intelligent thermal comfort management in smart buildings using a real-time adaptive RF–XGBoost ensemble model
Azher M. AbedAir Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University , Babylon 51001 ,Farrukh BakhritdinovKimyo International University in Tashkent , Shota Rustaveli str. 156, Tashkent 100121 ,Sardor SabirovMamun University , Bolkhovuz Street 2, Khiva 220900 ,Abdusalom UmarovDoniyor JumanazarovUrgench State University , Kh. Alimdjan Str. 14, Urgench 220100 ,Saira SakhabayevaAdvanced Research and Technology Group LLP , Astana ,Fohagui Fodoup Cyrille VinceslasDepartment of Renewable Energy Technology, College of Technology, University of Bamenda , Bamenda ,
2026en
ABI
Аннотация
Abstract Many existing thermal comfort prediction models rely on static control logic or single algorithms that adapt poorly to dynamic indoor conditions, creating a gap for adaptive, real-time approaches. This study proposes a hybrid ensemble model that combines Random Forest and XGBoost through dynamic weighting to improve predicted mean vote prediction and HVAC control. Using 52 034 sensor readings from four smart buildings, the model reduces root mean square error by 14% and improves R2 to 0.935 compared with baseline models while maintaining 88.9% accuracy in extreme conditions. Integrated into HVAC operation, it achieves 25.4% energy savings with a 1.81-year payback period and 176% return on investment.
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