Much is known about the increasing levels of energy consumption and environmental decay caused by the built environment. Also, more and more attention is shown to the energy consumption of dwellings, from the early design stage until the occupants start living in them. The increasing complexity of building technologies, the occupants’ preferences, and their needs and demands make it difficult to achieve the aimed energy consumption levels. The goal of reducing the energy consumption of dwellings and understanding the share of occupant behavior in it form the context of this research. Several studies have demonstrated the ‘energy performance gap’ between the calculated and the actual energy consumption levels of buildings, and have explored the reasons for it. The energy performance gap is either caused by calculation drawbacks, uncertainties of modeling weather conditions, construction defects regarding air tightness and insulation levels, or by occupant behavior. This research focuses on the last aspect, i.e. analyzing the relationship between occupant behavior and energy consumption in dwellings, understanding the determinants of energy consumption, and finding occupants’ behavioral patterns. There are several dimensions of occupant behavior and energy consumption of dwellings: dwelling characteristics including the energy and indoor comfort management systems, building envelope, lighting and appliances; occupant characteristics including the social, educational and economical; and actual behavior, including the control of heating, ventilation and lighting of spaces, and appliance use, hot water use, washing, bathing, and cleaning. Attempting to understand this complexity asks for a methodology that covers both quantitative and qualitative methods; and both cross-sectional and longitudinal data collection, working interdisciplinary among the domains of design for sustainability, environmental psychology, and building and design informatics.@en

In EU member states, the consumption of electricity per dwelling has remained more or less constant, although the consumption of large appliances has decreased considerably in the last 2 decades. This stabilization is caused by the increased ownership, usage and consumption levels of Information, Communication and Entertainment (ICE) appliances. This paper aims to analyze electrical appliance use in the Dutch housing stock, and identify behavioral patterns and profiles of electricity consumption. The analysis is conducted by applying descriptive, correlation, and exploratory factor analyses on data collected from 323 dwellings in two neighborhoods in the Netherlands. Our results show that behavioral patterns could be found based on actual occupant behavior of lighting and appliance use, especially depending on household activities like cooking, (personal) cleaning, etc. Behavioral profiles could be determined based on household and dwelling characteristics, i.e. household size, income, education, dwelling type, age, hours of working outside. The 4 profiles set up in this research are explained as ‘family,’‘ techie,’ ‘comforty,’ and ‘conscious.’ These profiles showed statistically significantly differences in terms of their electricity consumption levels.

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This paper focuses on the influence of occupant behavior on the energy performance of dwellings in the Dutch context. The aim of this study was to identify how the energy performance of a dwelling is sensitive to the behavior of its occupants. To this end, the study's methodology adopted the Monte Carlo method of analysis, one of the most commonly used means of analyzing the approximate distribution of possible results on the basis of probabilistic inputs. Data related to occupant behavior were generated from a survey of 313 households in The Netherlands. The Dutch reference row house was used to test the behavioral patterns, and the test results were determined through simulation-based modeling. The key input parameters were presence, heating control patterns (thermostat and radiators), and ventilation control patterns (windows, grilles, and mechanical ventilation), while minimum indoor resultant temperature and heating energy demand served as the key output parameters. The results showed that both heating energy demand and minimum indoor resultant temperature were most sensitive to the thermostat setting and most robust in relation to the radiator setting.

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