## 1 Introduction
According to [PERSON] [16], energy-usage simulations in models of dwellings in which the occupants' window opening and heating set-point adjustments are set by probabilistic functions (inferred from field measurements), were up to \(61\%\) higher than when the control system was simulated in a deterministic way using fixed schedules. In general, [PERSON] [17] claims that although building energy models have improved over the years, there have been no major improvements in the methods used to model occupants' behavior and that these methods require detailed monitoring of the building's occupants. According to [PERSON] [18], however, two more things are needed, i.e., time diaries and interviews, which together with logged measurements can be great tools to detect behavior that affects energy use in buildings. They can also be used to create detailed schedules and behavioral models for building energy-simulation models so that they are better at predicting actual energy use [18]. As regards the influence of building-occupant behavior on energy efficiency, [PERSON] [19] stated that introducing energy-conservation measures without taking into account user satisfaction can often be counter-productive, because users are likely to try to adapt their environment to have comfortable conditions. These kinds of actions can include some key changes, such as opening windows, shading maneuvers and adjusting the air temperature, which need to be anticipated. In this sense, a number of studies have recently been conducted dealing with occupants' behavior and factors influencing occupants' behavior and models [20; 21; 22; 23]. In his comprehensive study, [PERSON] [20] highlighted the ten most important issues regarding energy-related occupant-behavior research and applications in buildings. According to the research of [PERSON] [22], more accurate, reliable and realistic occupant-behavior models still need to be developed. One of them was developed by [PERSON] [23], i.e., a framework of occupant behavior with the aim to allow the incorporation of more accurate behavioral models into building-simulation tools to provide comparable metrics and results on: (1) the behavioral factors that impact building energy performance; (2) the potential energy savings from improved occupant behavior in buildings; and (3) the design of robust building operation scenarios, technologies, systems and retrofit strategies. IEA EBC Annex 66 is dealing specifically with the definition and simulation of building-occupant behavior. According to Annex experts, the occupant-behavior models developed by different researchers are often inconsistent, with a lack of consensus in common language, good experimental design and modeling methodologies [24]. In his work, [PERSON] [25] highlighted the key research issues, methods and outcomes contained in Annex 66. One important finding was that the methods for collecting data, which are fundamental to the modeling of occupant behavior, are evolving rapidly and simultaneously with sensors and ICT. Studies of the impact of real occupant behavior in buildings at the detailed level are scarce. In addition, as [PERSON] [10] found in his review, further analyses of the interactions between technological improvement and behavioral change are needed to influence the energy effect of occupant behavior, to better understand occupant behavior driven by feedback and anticipate actual building performance. Nevertheless, since occupant behavior is unpredictable, a variety of techniques are nowadays used in behavior-related modeling; for instance, agent-based modeling (ABM) and multi-agent systems (MASs), which have a clear record of success, and other predictive techniques, such as fuzzy modeling, neural networks and Markov chains. However, a review by [PERSON] [26] indicated that further research is needed on how these new techniques can be integrated into current energy-simulation software or developed as new software.