Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted with a Thermoelectric Air-Conditioning System
Subjective analysis of thermal comfort of occupants relates to the recording of the level of satisfaction or dissatisfaction of occupants with regard to indoor environmental conditions on a scale which ranges from -5 to +5. This requires recruitment of subjects and matching for gender, age etc. In t...
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| Main Authors: | , , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086307400&doi=10.1109%2fACCESS.2020.2985036&partnerID=40&md5=0b96939cf1438d7b962302dbd4737612 http://eprints.utp.edu.my/23326/ |
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| Summary: | Subjective analysis of thermal comfort of occupants relates to the recording of the level of satisfaction or dissatisfaction of occupants with regard to indoor environmental conditions on a scale which ranges from -5 to +5. This requires recruitment of subjects and matching for gender, age etc. In this study, we have tried to predict the thermal comfort of occupants by observing their real behavior inside the test room fitted with a novel thermoelectric air duct (TE-AD) cooling system rather than a conventional air conditioning system. Firstly, real experimental data were collected for more than two months from the test room equipped with the TE-AD cooling system operated at an input power supply of 6 A and 5 V. After that, the ANN model was developed based on the Levenberg-Marquardt algorithm by taking experimental parameters such as air temperature, relative humidity, globe temperature, wind speed, metabolic rate, and clothing value as model input. The ANN model is optimized by developing different models with different data points as a starting input in the training and validation process. The neuron optimization has been carried out in these models to minimize the mean square error (MSE) for the ANN model. The result shows that among the three models M1, M2, and M3, the optimum predictive mean value (PMV) was obtained from M1 at 10 neurons with MSE of 0.07956, while for predicted percentage dissatisfied (PPD), M3 gives optimum accuracy at 10 neurons with MSE value of 5.1789. The ANN model is then generalized to predict thermal comfort for one week and then for one month. Finally, all the model results were validated with the experimental data. © 2013 IEEE. |
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