According to the International Energy Agency, occupant Behavior is among the six driving factors of energy uses in Buildings. Therefore, it is widely considered a great influence on energy performance and must be studied concerning the influencing parameters, such as: Indoor and outdoor environmental quality, personal, cultural, and physiological factors; also, physical and structural conditions of the building should be included. However, the literature's studies show that this analysis is not frequently performed, bringing large impacts on the world's search for sustainable buildings. This paper aims to develop a strategy to incorporate the occupant's patterns and preferences at a residential level. This strategy is based on two different surveys applied to 41 occupants, considering influencing factors on the building performance. The data collected were analyzed by regression models to establish the significance of possible influencing variables, and the statistical correlations technique between variables is developed. The results show an important relationship between parameters and energy consumption.
This investigation proposes a methodology to predict indoor air temperature and CO2 levels. For this, a two-occupant office inside a building in the Technological University of Panama is taken as a case study and modeled in Designbuilder simulation software validated via experimental data. Here, a mathematical model that considers internal heat gains by the occupants and CO2 emissions, including physical characteristics and activities developed, is constructed via the thermal network (RC) and system identification approaches. Three linear grey-box models are identified: a 4R2C for cooling system mode, a 3R2C for natural ventilation conditions, and a 1R1C for CO2 model. The results showed that the identified model is useful for estimating the indoor air temperature under both modes: “natural ventilation on” and “cooling system on,” in separated situations. Thus, it is determined that by incorporating the internal heat gain generated by the occupant in the model identification process, the data set is closer to real values than implementing a standard value as suggested by the literature. On the contrary, the CO2 model allowed an approximation between estimated and real data, but this prediction must be developed in a non-linear model for better results.
Air quality plays a decisive role in the performance of the occupants considering that people spend at least 70% of their life indoors. This research aimed to determine if an air split unit in a small public office provide appropriate air quality for its employees. The ventilation performance was evaluated in passive and mechanical mode; the dynamic interface DesignBuilder simulated three case studies: the first one was to validate the data obtained with a temperature sensor during 10 workdays, the following two compared exclusively mechanical ventilation and exclusively natural ventilation with all windows and doors opened. The indicators utilized were CO2 concentration, indoor air renewal rates, and thermal comfort. The results showed that natural ventilation is insufficient to ensure high indoor air quality due to thermal discomfort, but acceptable CO2 concentrations were registered. In contrast, mechanical ventilation improved thermal comfort levels, but the CO2 concentration remained slightly outside the acceptable limits. These results demonstrated that the office is not designed to operate passively, restricting their functionality with mechanical ventilation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.