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

Domenico Palladino,Cinzia Buratti,Iole Nardi

doi:10.3390/en13174500

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.

Highlights

While buildings’ energy consumption can be evaluated by using different approaches and methods, which can lead to very close results, the available methodologies for thermal comfort evaluation can lead to greatly different results, since it depends on the people and on their subjective thermal perception of the environment
Energies 2020, 13, 4500 neural network (ANN) able to predict the thermal comfort inside a building before its realization, i.e., in the building design phase? These are the research questions that motivated this work and have been answered by developing an artificial neural networks (ANNs) for the predicted mean vote (PMV) assessment in summertime, using solely three input variables which can be derived from in-situ monitoring campaign or from another ANN
ANNs trained trained by by using using more input variables, while the second one allowed to choose the trained with neurons in more input variables, while the second one allowed to choose the ANN trained with 36 neurons in the hidden layer as the best trained with the highest global regression value, equal to the hidden layer as the best trained ANN with the highest global regression value, equal to 0.925

Summary

Introduction

Buildings’ design or energy retrofit interventions face two challenges: low energy consumption and thermal comfort conditions, both requiring a deep analysis of the building features and of the installed heating, ventilation and air conditioning (HVAC) system.While buildings’ energy consumption can be evaluated by using different approaches and methods, which can lead to very close results, the available methodologies for thermal comfort evaluation can lead to greatly different results, since it depends on the people and on their subjective thermal perception of the environment.Is it possible to apply artificial intelligence to thermal comfort problems? If so, does it allow one to accurately predict the thermal comfort indexes, compared to common methodologies? In this case, is it possible to reduce the input variables needed to estimate the indexes? Is this kind of artificialEnergies 2020, 13, 4500; doi:10.3390/en13174500 www.mdpi.com/journal/energiesEnergies 2020, 13, 4500 neural network (ANN) able to predict the thermal comfort inside a building before its realization, i.e., in the building design phase? These are the research questions that motivated this work and have been answered by developing an ANN for the predicted mean vote (PMV) assessment in summertime, using solely three input variables which can be derived from in-situ monitoring campaign or from another ANN. Buildings’ design or energy retrofit interventions face two challenges: low energy consumption and thermal comfort conditions, both requiring a deep analysis of the building features and of the installed heating, ventilation and air conditioning (HVAC) system. Is it possible to apply artificial intelligence to thermal comfort problems? Does it allow one to accurately predict the thermal comfort indexes, compared to common methodologies? Energies 2020, 13, 4500 neural network (ANN) able to predict the thermal comfort inside a building before its realization, i.e., in the building design phase? These are the research questions that motivated this work and have been answered by developing an ANN for the predicted mean vote (PMV) assessment in summertime, using solely three input variables which can be derived from in-situ monitoring campaign or from another ANN.

Objectives

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Discussion

Conclusion