E3S Web Conf.
Volume 356, 2022The 16th ROOMVENT Conference (ROOMVENT 2022)
|Number of page(s)||3|
|Section||Thermal Comfort and Natural Ventilation|
|Published online||31 August 2022|
Individual thermal comfort models based on optimized BP neural network algorithms
1 Xi’an University of Architecture and Technology, China.
2 Tianjin Chengjian University, China.
* Corresponding author: firstname.lastname@example.org (B. Yang)
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.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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