Thermal comfort estimation using a neurocomputational model

Thermal comfort conditions are important for the normal development of human tasks, and as such they have been analyzed in the context of several areas including human physiology, ergonomics, heating and cooling systems, architectural design, etc. In this work, we analyze the estimation of the therm...

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Bibliographic Details
Published in2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI) pp. 1 - 5
Main Authors Rodriguez-Alabarce, Jose, Ortega-Zamorano, Francisco, Jerez, Jose M., Ghoreishi, Kusha, Franco, Leonardo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Constructive Neural Networks
Data models
Estimation
Mathematical model
Neural networks
Neurons
Supervised learning
Temperature
Thermal Comfort
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DOI10.1109/LA-CCI.2016.7885703

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Summary:Thermal comfort conditions are important for the normal development of human tasks, and as such they have been analyzed in the context of several areas including human physiology, ergonomics, heating and cooling systems, architectural design, etc. In this work, we analyze the estimation of the thermal comfort perception by human subjects using a neurocomputational model based on the C-Mantec constructive neural network architecture, comparing it with two standard methods for modeling thermal comfort: Fanger and COMFA models. The results indicate a significative advantage of C-Mantec in terms of the predictive accuracy obtained, consider also that the flexibility of the neural model would permit the introduction of extra variables that can increase further the thermal comfort estimation.
DOI:10.1109/LA-CCI.2016.7885703