Abstract
Occupant behavior towards heating and cooling system setting is a very complex process that has been under investigation in the past years. As most of dynamic energy simulation tools consider energy consumption as fully deterministic with fixed and unrealistic schedules, the ability to predict properly the energy consumption is poor because of occupant interaction with indoor environment. In this study, the occupant in residential buildings is modeled as a probabilistic process. The occupant behavior related to thermostat settings is studied through experimental measurements collected in eleven buildings in France over a period of one year, by monitoring various parameters, including indoor air temperature, ambient temperature, indoor and outdoor relative humidity and indoor CO2. The occupant attitude was classified into three groups, active, normal and passive, according to the number of setting changes per year. The Logistic regression is adopted to calculate the probability of changing the thermostat setting by an occupant, in terms of different environment parameters. The results yield to a proposed model that can be implemented in simulation software, in order to take into account the occupant behavior in the assessment of realistic energy consumption.
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