Occupants’ behavioural diversity regarding the indoor environment in social housing. Case study in Northern Spain

Opportunities and Barriers of Calibrating Residential Building Performance Simulation Models Using Monitored and Survey-Based Occupant Behavioural Data: A Case Study in Northern Spain

“…The development of the CM combines the "normative data" and "OB real data", replacing the most influential indoor factor variables of the "normative data" with "OB real data", calibrating the energy model. The "OB real data" are derived from the monitorization and surveys to identify occupancy and heating patterns investigated by a previous study by S. Perez-Bezos et al [32]. The behavioural data are based on patterns of actual use of dwellings.…”

“…The behavioural data are based on patterns of actual use of dwellings. In order to introduce the diversity of behaviour that can exist in the same building, the methodology and results of the previously mentioned research [32] have 6 of 19 been used. This research proposed the definition of profiles from heating consumption data based on time series clustering.…”

“…These clusters define the associated environmental and consumption parameters: occupancy (density and schedules) and heating consumption (setpoint, setback, and schedule). This way, the "occupant behaviour data" are a set of OB clusters reflecting the behavioural patterns of the occupant of the case study defined by the previous study [32] that will be applied for the calibration of the CM. However, the non-calibrated parameters by real data apply the "normative data", such as the ventilation and the internal gains.…”

“…The data collected in the previous stage are introduced as input parameters organised in three parameter groups (E, I, O) for two types of models, the NM and CM, following Table 1. For the two types of energy models, on the one hand, one single NM is developed; on the other hand, a set of CMs are developed: n number of CMs for n number of the diverse OB clusters defined in the indoor parameters collection data (see Figure 3), based on the previous study [32]. The simulation process runs the BPSs with the developed models and analyses the resulting outputs.…”

Energy Conservation at Home: A Critical Review on the Role of End-User Behavior

“…In particular, the relationship between income and energy consumption in the residential sector is also investigated [77,[118][119][120][121][122], with special concern for social housing occupants [123]. The income-energy use relationship is often examined along with other contextual factors, such as age and employment status, as is the case of the research of Godoy-Shimizu et al, who found that according to statistical analyses, high electricity use is significantly correlated with social class, large household size, unemployment, and middle age, while low electricity use is significantly correlated with single-person households, small homes, and retirement [124].…”