Individual thermal comfort models based on optimized BP neural network algorithms

• Published in: E3S web of conferences Vol. 356; p. 3020
• Main Authors: Li, Xiaojing, Yang, Bin
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2022
• Subjects: Accuracy; Algorithms; Back propagation; Back propagation networks; Bayesian analysis; Data compression; Heart rate; Indoor air quality; Indoor environments; Mathematical models; Model accuracy; Neural networks; Optimization algorithms; Parameters; Physiology; Propagation; Regularization; Thermal comfort; China
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202235603020

Thermal comfort plays an important role in human life and it affects occupant satisfaction, health, and productivity. Individual differences are not considered in traditional control strategies based on temperature setpoints. The reality is that operators often expend more energy to maintain the indoor environment and the thermal satisfaction of occupancy is not as well as expected. Thus, individual thermal comfort models based on physiological parameters and environmental parameters were presented using the back-propagation (BP) neural network. Moreover, we used three training algorithms including Levenberg-Marquardt (L-M), Bayesian Regularization, and Scaled Conjugate. We observed that using the L-M algorithm resulted in slightly better performance (R=0.96) than other algorithms. The precision results suggest that the BP network algorithm is an effective approach for real-time predicting thermal comfort. In the follow-up study, we would focus on feature engineering (feature selection) and introduce appropriate variables (e.g., heart rate) to improve the model’s accuracy.