TY - GEN
T1 - Machine Learning Techniques to Predict Real Time Thermal Comfort, Preference, Acceptability, and Sensation for Automation of HVAC Temperature
AU - Acquaah, Yaa T.
AU - Gokaraju, Balakrishna
AU - Tesiero III, Raymond C.
AU - Roy, Kaushik
PY - 2022/1/1
Y1 - 2022/1/1
N2 - The control of Heating, Ventilation, and Air Conditioning (HVAC) system automatically is one of the progressive areas of research. The collective importance of the HVAC system is to maintain indoor thermal comfort while ensuring energy efficiency. This study explores the thermal comfort, acceptability, preference, and sensation of fifteen subjects from February to September 2021. Multiclass-multioutput Decision Tree, Extra Trees, K-Nearest Neighbors and Random Forest classification models were developed to predict the thermal comfort metrics, of subjects in a room based on gender, age, indoor temperature, humidity, carbon dioxide concentration, activity level and time series features. It is important to understand occupants’ thermal comfort in real time to automatically control the environment. The best mean accuracy and mean squared error of 68% and 2.15 respectively was achieved by multiclass-multioutput Extra Tree classification model, when all the features were used in training and testing. Through this study, the feasibility of using machine learning techniques to predict thermal comfort, preference, acceptability, and sensation at the same time for HVAC control was established.
AB - The control of Heating, Ventilation, and Air Conditioning (HVAC) system automatically is one of the progressive areas of research. The collective importance of the HVAC system is to maintain indoor thermal comfort while ensuring energy efficiency. This study explores the thermal comfort, acceptability, preference, and sensation of fifteen subjects from February to September 2021. Multiclass-multioutput Decision Tree, Extra Trees, K-Nearest Neighbors and Random Forest classification models were developed to predict the thermal comfort metrics, of subjects in a room based on gender, age, indoor temperature, humidity, carbon dioxide concentration, activity level and time series features. It is important to understand occupants’ thermal comfort in real time to automatically control the environment. The best mean accuracy and mean squared error of 68% and 2.15 respectively was achieved by multiclass-multioutput Extra Tree classification model, when all the features were used in training and testing. Through this study, the feasibility of using machine learning techniques to predict thermal comfort, preference, acceptability, and sensation at the same time for HVAC control was established.
KW - Multiclass-multioutput
KW - Thermal acceptability
KW - Thermal comfort
KW - Thermal preference
KW - Thermal sensation
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85137993268&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85137993268&origin=inward
U2 - 10.1007/978-3-031-08530-7_55
DO - 10.1007/978-3-031-08530-7_55
M3 - Conference contribution
SN - 9783031085291
VL - 13343 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 659
EP - 665
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Fujita, Hamido
A2 - Fournier-Viger, Philippe
A2 - Ali, Moonis
A2 - Wang, Yinglin
PB - Springer Science and Business Media Deutschland GmbH
CY - deu
T2 - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022
Y2 - 19 July 2022 through 22 July 2022
ER -
