Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.

Most of the window use models developed for building energy simulations include physical environmental variables as drivers. Even though it is a common approach to include these variables, the validity of this approach can be questioned based on observations in the literature regarding the consistency and seasonality of the relationship between these variables and the window use. Additionally, it was questioned whether this approach could capture the inter-occupant diversity. To assess the extent of the uncertainties, modelling methods from the literature were applied to a case study. The results revealed that a large share of the variance in window use can be attributed to the season and the household-ID. This indicates that the window use varied across the year but was stable within one season, and that these behaviours differed significantly between households. Thus occupants interacted with windows according to seasonal habits, more so then according to momentaneous environmental conditions.

The use of solar shading can have an important influence on the internal heat gains, especially in zero-energy buildings. However, the research in literature is almost uniquely focused on offices, while information on the use of solar shading in residential dwellings is lacking. Therefore, the solar shading behaviour of occupants of a nearly zero-energy social housing neighbourhood in Belgium is analysed. Data are gathered by solar shading logging with a building monitoring system, logbooks and cross-sectional surveys. In general, the solar shades were not often adjusted, with many of the solar shades either always opened or always closed. Clear seasonal influences were observed; however, the temperature and solar irradiance did not reveal a significant relationship with the use of the solar shading. This relationship could be biased by the fact that some of the occupants use the solar shades not to prevent solar heat gains but to darken the room in the evening (blinds). Since the shades are not often adjusted and are thus in the same position for a long time, the use is independent of the prevailing weather conditions. The position of the solar shades seems to be more influenced by the personal preference of the occupant than by external factors. Additional simulations, carried out with Modelica, showed that when the full capacity of the solar shades is used, the overheating can be decreased up to 29% in south oriented rooms. The possible negative impact on the heating demands can be neglected. This shows the necessity to correctly model the solar shading behaviour in residential buildings, especially in nearly zero-energy buildings, for which the cooling demands are increasingly important.

Clothing has a direct influence on the thermal comfort of an occupant and so, indirectly on the energy use of a building. Literary sources point out a lack of data about clothing behaviour in residential buildings. In order to assess the clothing behaviour two kinds of surveys are created: logbook surveys and online questionnaires. Both surveys are executed between March 11 and April 5, 2019. The mean clothing insulation worn during the investigation period is 0.58 clo. This clo-value differs from the clothing insulation values provided by Fanger, which are 1.0 clo for winter months and 0.5 clo for summer months. The influence of the indoor temperature, outdoor temperature, weather history memory, gender and age on the clothing behaviour is analysed. All variables have a small significant influence on the clo-value. It was found that occupants tend to wear the same clothes when they are at home. So, each participant clothes him/herself to be comfortable in their clothes and in the temperature of their own room. People who are used to live in lower indoor temperatures will, and are used to, wear more clothing insulation to be thermally comfortable than people living in warmer indoor temperatures. An adjustment in clothing behaviour can make a big impact on the energy use of residential buildings. A decrease in indoor temperature of 1°C can lead to heating energy savings of 10%. To remain thermally comfortable, the occupant must only wear an extra insulation value of 0.17 clo, which corresponds with a shirt. The question remains if occupants will effectively use the opportunity of changing clothes to lower their energy use.

In this paper, we discuss the presence of habits in the window opening behaviour of social housing tenants in a nearly zero-energy development in Belgium. A window opening habit can be defined as an action with a window that is repeated daily around the same time independently of the prevailing weather conditions. A carbon neutral social housing estate (106 apartments and 90 single family dwellings) was used as a test case. Questionnaires, window opening logging with a building monitoring system and cross-sectional surveys were used to collect window opening data. A method to identify window opening habits is determined. Up to 45% of the occupants act on some sort of habit in wintertime, predominantly in the bedrooms and in the morning. In summer these habits dissipate due to very long window openings. Weather variables and indoor climate parameters, traditionally used as the basis for window opening behaviour models, are rather poor predictors of opening behaviour in winter. The incorporation of habits in window opening models can lead to more reliable predictions of window opening behaviour.