Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm

Domenico Palladino, Iole Nardi and Cinzia Buratti
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Domenico Palladino: Italian National Agency for New Technologies, Energy and Sustainable Economic Development of Engineering (ENEA), Via Anguillarese, 301, S.M. di Galeria, 00123 Rome, Italy
Iole Nardi: Italian National Agency for New Technologies, Energy and Sustainable Economic Development of Engineering (ENEA), Via Anguillarese, 301, S.M. di Galeria, 00123 Rome, Italy
Cinzia Buratti: Department of Engineering, University of Perugia, Via G. Duranti 63, 06125 Perugia, Italy

Energies, 2020, vol. 13, issue 17, 1-27

Abstract: A simplified algorithm using an artificial neural network (ANN, a feed-forward neural network) for the assessment of the predicted mean vote (PMV) index in summertime was developed, using solely three input variables (namely the indoor air temperature, relative humidity, and clothing insulation), whilst low air speed (<0.1 m/s), a minimal variation of radiant temperature (25.1 °C ± 2 °C) and steady metabolism (1.2 Met) were considered. Sensitivity analysis to the number of variables and to the number of neurons were performed. The developed ANN was then compared with three proven methods used for thermal comfort prediction: (i) the International Standard; (ii) the Rohles model; (iii) the modified Rohles model. Finally, another network able to predict the indoor thermal conditions was considered: the combined calculation of the two networks was tested for the PMV prediction. The proposed algorithm allows one to better approximate the PMV index than the other models (mean error of ANN predominantly in ±0.10–±0.20 range). The accuracy of the network in PMV prediction increases when air temperature and relative humidity values fall into 21–28 °C and 30–75% ranges. When the PMV is predicted by using the combined calculation (i.e., by using the two networks), the same order of magnitude of error was found, confirming the reliability of the networks. The developed ANN could be considered as an alternative method for the simplified prediction of PMV; moreover, the new simplified algorithm can be useful in buildings’ design phase, i.e., in those cases where experimental data are not available.

Keywords: artificial neural networks; thermal comfort; predicted mean vote calculation; indoor thermal conditions; clothing insulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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