Tyndall, A., Cardell-Oliver, R., Keating, A.: Occupancy estimation using a low-pixel count thermal imager. 237, 110810 (2021)īeltran, A., Erickson, V., Cerpa, A.: ThermoSense: occupancy thermal based sensing for HVAC control, pp. Wang, C., Jiang, J., Roth, T., Nguyen, C., Liu, Y., Lee, H.: Integrated sensor data processing for occupancy detection in residential buildings. Weber, M., Doblander, C., Mandl, P.: Towards the Detection of Building Occupancy with Synthetic Environmental Data (2020) Vela, A., Alvarado-Uribe, J., Davila, M., Hernandez-Gress, N., Ceballos, H.G.: Estimating occupancy levels in enclosed spaces using environmental variables: a fitness gym and living room as evaluation scenarios. Kumar, S., Singh, J., Singh, O.: Ensemble-based extreme learning machine model for occupancy detection with ambient attributes. In: Proceedings of the 2020 Applied Imagery Pattern Recognition Workshop (AIPR), 13–15 October, pp. 13(19), 3847 (2021)Īcquaah, Y., Steele, J.B., Gokaraju, B., Tesiero, R., Monty, G.H.: Occupancy detection for smart HVAC efficiency in building energy: a deep learning neural network framework using thermal imagery. ISO_7730, Ergonomics of the Thermal Environment -Analytical DeterminationĪcquaah, Y.T., Gokaraju, B., Tesiero, R.C., Monty, G.H.: Thermal imagery feature extraction techniques and the effects on machine learning models for smart HVAC efficiency in building energy. KeywordsĪNSI/ASHRAE Standard 55–2017, Thermal Environmental Conditions for Human Occupancy 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. 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. It is important to understand occupants’ thermal comfort in real time to automatically control the environment. 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. This study explores the thermal comfort, acceptability, preference, and sensation of fifteen subjects from February to September 2021. The collective importance of the HVAC system is to maintain indoor thermal comfort while ensuring energy efficiency. The control of Heating, Ventilation, and Air Conditioning (HVAC) system automatically is one of the progressive areas of research.
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