## 1 Introduction
### Occupant Behavior  
Buildings, like vehicles, are driven by their occupants. Several international studies have documented that energy use in buildings is closely linked to occupant presence, movement and interaction with building energy devices and systems [22; 23; 24]. The assumed occupant behaviors, setpoints and indoor conditions as well as other simplified assumptions in building energy calculations will result in significant deviations between the estimated and actual energy use. As a result, these deviations make it even more challenging for policy makers to realistically assess the impacts of incentive policies and make accurate projections of future energy use in the building sector. A previously published detailed literature review underlined the need for more empirical data in order to improve predictions by calibrating models [25]. More recent studies have also reviewed evidence from the literature in an effort to quantify the role of the occupants' behavior in the actual energy performance of a building [26]. The deviations ranged from \(-38\%\) to \(+96\%\), although there were deviations among the various studies that followed different approaches in the range and quality of data, the normalization procedures and other factors that may have led to different conclusions. Occupant behavior results in higher or lower energy consumption by interacting with the building's heating, cooling and lighting systems which, in turn, results in different indoor conditions. This gap has been quantified by several studies. On a building scale, the approach to bridging the gap is to calibrate detailed simulations by creating realistic digital twins [27] to accurately assess building energy performance, accurate indoor conditions and energy use by following the available calibration methodologies [28]. For large building portfolios and building stock modeling, this is a cumbersome approach and a need more practical and general approaches. Among the different approaches, the performance gap has been quantified using data from energy performance certificates [29]. On one hand, EPC intrinsic errors, including procedural differences, experience among energy experts performing the energy audits and assumptions for missing data are some of the reasons behind a variation of \(\pm 20\%\) in the assessment of a building's energy consumption [30]. On the other hand, the overarching observation from comparing the actual and calculated energy consumption from EPCs is that low performance buildings use significantly less energy than what was calculated (usually referred to as the prebound effect), while buildings with high performance use somewhat more energy than expected (usually referred to as the rebound effect). For example, in Greece, dwellings may use less energy than calculated by about \(-46\%\) in houses and \(-43\%\) in multi-family apartments [13]. In Switzerland, the prebound effect was quantified at \(-23\%\)[31]. When comparing French and German low-performance dwellings, on average, the calculated energy use of French houses was lower (i.e., under-consuming) compared with German ones, leading to prebound effects of \(-7.4\%\) compared with \(-13.9\%\), respectively [32]. The discrepancy increased to \(-31.5\%\) for French houses, compared with \(-10.5\%\) for German houses, for better energy performance buildings. A high prebound effect and low income is usually related to fuel poverty. Low-performance and inefficient buildings are usually occupied by people with low incomes, suffering from fuel poverty [33] and facing financial difficulties that prohibit them from paying for the energy that is necessary to maintain a proper IEQ or for an energy renovation of their dwelling. Similar deviations were also observed in Irish houses, with actual energy savings of \(-22\%\) for low-performance dwellings and even reaching up to \(-70\%\) for high (very good) performance dwellings [34].