Thermal comfort and use of thermostats in Finnish homes and offices

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

“…Starting with the premise that, when experiencing discomfort, "people react in ways which tend to restore their comfort"-such reaction is described as adaptive behavior [35]. Field studies show ample evidence of adaptive behavior displayed through the use of various thermal control devices available in buildings [36][37][38][39][40][41]. Opening windows for a cool breeze and turning up the thermostat to heat the room are examples of adaptive behavior.…”

A critical review on questionnaire surveys in the field of energy-related occupant behaviour

“…However, Veitch, et al claimed that the sample size was still too low to report any statistical tests associated with cross-tabulations by region or by construction year on their dataset. Karjalainen used software (SPSS) to perform the statistical analyses on 3094 samples (Karjalainen 2009). The Wilcoxon signed-rank (2-tailed) test was used for interval data to determine if a significant difference existed between the home and office environments.…”

“…The age of occupants and number of children affected heating expenses as well. (Meier and Rehdanz 2010) Gender differences were studied regarding thermal comfort, temperature preference, and the use of thermostats (Karjalainen 2009). Females generally preferred higher room temperatures, but at the same time, they felt uncomfortably hot more often than males.…”

An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework

“…Another data collection approach is to ask occupants to provide information through self-reporting [26] or by using different interview techniques such as questionnaires [23, 24, 29-31, 37, 50, 52, 55, 65, 73, 77-87], web-based questionnaires [88], computer-assisted telephone interviews [33,89] or mail surveys [90]. Questionnaire surveys are often used to identify the most important factors affecting occupant interactions with building control systems including: window opening behavior [23,24,26,55,73,[76][77][78]88], the use of heating [26,33,83,85,89] and cooling [37,84,87], solar shading and blinds [20,26,29,65], electrical lighting [26,31,81] and equipment [52,86].…”

“…as well as from weather stations (outdoor temperature and relative humidity, wind speed, rainfall, solar radiation and solar hours, etc.). Data collection techniques often include direct monitoring of the building control systems, using magnetic switches for windows [22][23][24][25][26], electromechanical sensors for shading systems, blinds and electric lighting [27][28][29][30][31][32], recording TRV (thermostatic radiator valve) switches [26,[33][34][35], AC thermostat set points [36,37], presence detectors such as motion sensors [38][39][40], intelligent control of building systems and real-time building visualization [41], security systems [42], PIR (passive infrared) sensors [43], ultrasonic detectors for light switching [44] , and smart/wireless electric outlet meters [45][46][47][48][49][50][51][52]. Occupant behavior can also be indirectly monitored by sensing 'secondary' environmental variables, parameters or actions and then performing extrapolation.…”