An experimental machine learning analysis on adaptive thermal comfort and energy management in hospitals

The Healthcare sector has known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort indoor conditions, catering for patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analysed in conjunction to the Thermal Comfort Questionnaire Surveys where the participants including patients, doctors, nurses, and hospital staff were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset including experimental and survey-based responses were further analysed in the development of a correlation between operative temperature, operative relative humidity, and operative indoor air velocity with predicted mean vote and predicted percentage of dissatisfied thermal comfort parameters. The adaptive temperature, adaptive relative humidity, and adaptive indoor air velocity were estimated using the seasonal data set gathered during the summer – hot and dry, and hot and humid climate conditions. The Machine Learning Linear Regression Algorithm was incorporated to train operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML based adaptive thermal comfort model was proposed and developed in our study.