Modelling occupant behaviour

The number of behavioural variables contributing to final household energy demand results in a complex system, especially for space heating, which is not directly driven by occupancy and for which settings depend mostly on occupant choices linked both to thermal comfort and system control. The main parameters considered for the determination of energy demand profiles in design and modelling tools are based on fabric heat loss: building type and building thermal behaviour; heat production: heating system type and efficiency; electricity consumption: ap- pliance ownership, efficiency, lighting usage and bulb type; heat loss through ventilation: cold air infiltration and assumed ventilation rates for natural ventilation; and other variables which depend on householder choice, and impact upon energy consumption. These include but are not limited to: system control and thermostat settings, occupancy, temperature choice, natural ventilation and hot water consumption for showers and taps (Summerfield et al., 2007; Boait et al., 2012; Gram-Hanssen, 2010; Santin, 2011; Santin et al., 2009; Guerra-Santin and Itard, 2010; Shipworth et al., 2010).

The reduction measures analysis is limited by the dearth of hard data with which to develop and validate these models, taking into account occupant behaviour. This difficulty in collecting real data has produced an over reliance on theoretical predictions for many years (Oreszczyn et al., 2006; Cheng and Steemers, 2011; Natarajan et al., 2011). Although there are studies which have developed behavioural variables, there is still much work needed to systematically represent observed human behaviour and the way people actually live in the home environment (Bourgeois et al., 2006).

The challenge of building energy analysis is to move beyond controlled activity profiles and predefined scenarios towards prediction tools that account for the complexities of everyday life (Porteous et al., 2012; Malkawi and Augenbroe, 2004; Firth et al., 2008; Zimmermann et al., 2012). Human behaviour has been accounted for in simulation studies through the notion of ‘lifestyle constraints’, considering individuals’ consumption as the result from a range of contextual factors, such as building type, appliance characteristics, lifestyle choices, work, school and leisure, and the social and cultural values that are placed on daily activities (Kashif et al., 2013; Haldi and Robinson, 2011; Wall and Crosbie, 2009; Porteous et al., 2012; Wilhite et al., 1996). A consideration of lifestyle constraints has important implications for understanding the domestic context, given that it shows the complexity and number of variables that might interact to determine how energy is consumed. While building simulation tools require an element of simplification, there is an increasing need to represent patterns and interrelations that go beyond the consideration of individuals (Herkel et al., 2008; Toftum, 2010) and individual appliance use (Richardson et al., 2008), and instead take into account wider practices such as ventilation routines or space heating control (Hughes et al., 2009). More comprehensive and sophisticated models are needed to prevent the kinds of forgone conclusions that derive from narrowly defined notions of causality, especially for the design of future energy reduction measures in the home whose effectiveness will be highly influenced by users’ everyday choices (Kane et al., 2015).

4.1.3 Ventilation

Current models do not offer a good representation of ventilation rates in occupied dwellings (Hoes et al., 2009). So far, studies aiming at implementing realistic ventilation behaviour patterns in simulation programs have been focused on occupant behaviour in office buildings (International Energy Agency, 2013). This becomes problematic when tailoring information to home owners and estimating the impact of retrofit measures on energy consumption for specific households. Air change rates have a significant impact on building energy consumption, and the energy use linked to householders’ habits on natural ventilation can be over/underestimated using modelling tools, which relies on a limited number of inputs to assess how people ventilate the house (Henderson and Hart, 2007).

The air change rate N is a measure of the air volume added or removed from a space. It is an absolute value relative to the volume of space and it is calculated in existing models by assuming a number of Air Change Rates (ACH) based on an estimation of the dwelling infiltration rate, occupant window openings and minimum health standards for ventilation. Current models

rely on poor assumptions to estimate the air change rate, as this value is highly dependent on occupants’ behaviour, especially in domestic dwellings (Henderson and Hart, 2007; Jones et al., 2001; U.S Department of Energy, 2015; Birdsall et al., 1990; University of Wisconsin–Madison. Solar Energy Laboratory and Klein, Sanford A, 1979).

Empirical ventilation studies have shown behavioural parameters to be strong predictors (Bek et al., 2011; Howard-Reed et al., 2002). Howard-Reed found that the highest building ACH variability, was given by the opening of windows (Howard-Reed et al., 2002); In further research, Howard-Reed, reported that 63% of the average air change rate in 16 Denmark homes was due to occupants opening windows and doors (Howard-Reed et al., 2002). Also, householder characteristics such as age, gender and comfort preferences have been found to be influences upon ventilation actions, as well as building characteristics and type of room. For example, bedroom windows are more frequently opened in domestic buildings. Further to that, occupants’ ventilation behaviour is strongly determined by climatic variables and routines; for example, a number of studies found correlation between the number of openings and the season, time of the day, outdoor temperature, solar radiation and wind velocity (International Energy Agency, 2013).