2026-05-31T18:48:41Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/402652026-02-03T11:30:16Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Ortega-Zamorano, Francisco author Jerez-Aragonés, José Manuel author Rodríguez-Alabarce, José author Goreishi, Kusha author Franco, Leónardo author 2025-07-26 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. 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). https://hdl.handle.net/10630/40265 10.1007/s00500-025-10676-y Informática suave Redes neuronales (Informática) Temperatura - Control Modelos matemáticos Aprendizaje automático (Inteligencia artificial) Efficient thermal comfort estimation employing the C-Mantec constructive neural network model.