RT Journal Article
T1 Efficient thermal comfort estimation employing the C-Mantec constructive neural network model.
A1 Ortega-Zamorano, Francisco
A1 Jerez-Aragonés, José Manuel
A1 Rodríguez-Alabarce, José
A1 Goreishi, Kusha
A1 Franco, Leónardo
K1 Informática suave
K1 Redes neuronales (Informática)
K1 Temperatura - Control
K1 Modelos matemáticos
K1 Aprendizaje automático (Inteligencia artificial)
AB Thermal comfort is the condition in which a person feels satisfaction with the thermal environment through a subjective evaluation. In this work, a compact and efficient estimation of thermal comfort perception by human subjects is performed using a constructive neurocomputational model trained with data generated in controlled conditions with 49 volunteers giving 705 different scenarios, allowing, thanks to the versatility of the model, an interpretable and simple resulting function facilitating an easy handling of the results by people from different fields. The results have been compared with two of the most used standard methods for modelling thermal comfort: Fanger and COMFA models, and they show an improvement in terms of accuracy and mean square error both in a binary decision scenario (comfort or not) as well as for a discrete decision-making case in which different thermal comfort regions are considered. The flexibility of the neural model permits the incorporation of extra subject-related variables that increases further the thermal comfort estimation and, also, permits the implementation of the model in distributed and low cost/low consumption systems.
PB Springer
YR 2025
FD 2025-07-26
LK https://hdl.handle.net/10630/40265
UL https://hdl.handle.net/10630/40265
LA eng
NO Ortega-Zamorano, F., Jerez, J.M., Rodríguez-Alabarce, J. et al. Efficient thermal comfort estimation employing the C-Mantec constructive neural network model. Soft Comput 29, 4673–4684 (2025).
NO https://openpolicyfinder.jisc.ac.uk/id/publication/28648
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 1 mar 2026