NDLI: Analysis of thermal comfort with predicted mean vote (PMV) index using artificial neural network

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Analysis of thermal comfort with predicted mean vote (PMV) index using artificial neural network

Content Provider	Scilit
Author	Dyvia, H. A. Arif, C.
Copyright Year	2021
Description	Journal: Iop Conference Series: Earth and Environmental Science The thermal comfort standard can be accessed with a predicted mean vote (PMV) index that has six variables: air temperature, air velocity, relative humidity, mean radiant temperature, clothing insulation, and metabolism rate. Calculation of the thermal comfort of a room issued with a PMV index is quite complicated, considering the determination of the effect parameters can take a long time because it has a non-linear relationship. The purpose of this study is to analyze the sensitivity parameters that affect thermal comfort and to develop an artificial neural network model to predict the thermal comfort of the PMV index with two different input scenarios. Sensitivity analysis of thermal comfort uses the rank Spearman method, whereas the modeling of artificial neural networks uses the backpropagation method using a total of 784 data as data training and testing. Based on the results of sensitivity analysis shows metabolism rate and clothing insulation have a significant effect on the sensitivity of thermal comfort. Prediction of thermal comfort with the PMV index using an artificial neural network is very effective and has higher accuracy. The results are proven by validating the two model scenarios with an $R^{2}$ value of 0.99 and low RMSE value.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/622/1/012019/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/622/1/012019
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	1
Volume Number	622
Language	English
Publisher	IOP Publishing
Publisher Date	2021-01-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science Artificial Neural Network Thermal Comfort Index Using Using Artificial
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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