The performance of practices can often coincide with energy demand (Friis and Haunstrup, 2016). A review of energy studies by Cayla and Maïzi (2015) stated that explanatory variables such as the number of occupants and the occupancy patterns are usually insufficient in explaining the variation in demand across households. I adopted practices of the households as the unit of investigation for this study’s cohort. This use was based on the premise that there are no “average consumers” and the inter-household variation in demand has been widely attributable to the use that occurs (Brown et al., 2017), or, in other words, to “what people do.” This variation has been widely observed to be consistent in energy
75 research in recent times (Bell et al., 2015). The ontological investigation of practice theory suggests that both the practices, and their enactment are “iterative” (Lindahl and Folkesson, 2012). Practice theories have been summarised as being aimed at “flattening the relationship between scientific and common-sense knowledge” (Nolas, 2014). I partly adopted the Shatzkian approach to practices by examining what people do.
Some researchers Shove and Walker (2010); Mattioli et al. (2014); Higginson (2015) conceptualised the relationship between social practices and energy demand. This thesis focuses solely on “practices as performances” as detailed in (Schatzki, 1996). This approach was adopted to establish the relationship between households’ doings and electricity demand (intensity). Energy demand has been described as “pervasive” and “entangled” in the enactment of practices by Ozaki and Shaw (2014) who also noted that the inherent characteristics of sustainable technologies affect how everyday practices are enacted. An example of this is the existence of programmable washing machines. The use of this implies that the practice of washing can be done at any time and without the active occupancy or engagement by the user as at the time the washing machine runs.
The invisibility of electricity demand is widely acknowledged (Ellegård and Palm, 2011, Naus, 2016). I argue that a practice approach reduces the invisibility of electricity use as it highlights the “doings” that are responsible for the kWh readings. Aune et al. (2016) argued that everyday life routines are slow to change as they are embedded with comfort and convenience. Thus, I expect that the performance of the practices by this study’s cohort over the course of one week
76 represents their everyday life within the temporal frame of one week. Domestic electricity consumption is involved in the operation of the majority of appliances in the home (Wood and Newborough, 2003, Chatzitheochari, 2017). These operation patterns are a direct result of the routines of the households.
From the above, I argue that what people do has changed over longer temporal frames of which is beyond the scope of this study (Pantzar, 2010). For instance, ONS (2014) showed that online shopping by over 65s rose by about 400% between 2006 and 2014. These changes in device use practices have a direct impact on meter readings. Based on ONS (2014), I expect computer use to increase for specific activities such as shopping by older people. This is a gradually evolving practice that affects load profiles. As explained earlier, in this study, I investigated patterns of loads and demand intensity and appliance use that occurred over a week.
Three examples show the dynamics and difficulties of relating practices to residential electricity demand. First, domestic loads have grown in recent years. The most significant in the last decade has been in ICT which this study partly investigated. Røpke and Christensen (2012) projected a 250% growth by 2030 globally although Trust (2011) suggests growth figures were overestimated. Other emerging technologies such as the electrification of cars set a clear trend for a rise in domestic electricity consumption. For example, in India, recent policy direction towards banning petrol cars in favour of hybrid and electric cars and car manufacturers complying indicates a clear upward trend. In France and other European countries, government vehicle policy and car manufacturers indicate a
77 stronger presence of electric cars in the very near future. Also, in the UK, favourable taxations such as potential lower or zero-emission taxes payable are gradually becoming a more prominent unique selling point for new cars by manufacturers. The impact of the electrification of cars is beyond the scope of this study.
Second, devices are increasingly multifunctional, and multitasking can occur during use. These make it imperative and more difficult to identify the specific activity that is carried out when an appliance is turned on. For example, a television can now access radio stations in addition to other multifunctional features appliances now generally possess. These specific activities can give an insight into the potential trend for appliance use. The observation of this specific practice is beyond the scope of this research.
Third, types of load profiles are usually linked to time-space and especially the calendar week. Gottwalt et al. (2011) identified 3 load profile types: weekday, Saturday and Sunday which result in unique demand rhythms. Their findings may differ when considering the specificities of different household typologies, such as the growing proportion of solo living and a growing number of single and two-person older households. Other factors such as flexible working, weekend work and working from home (which has a rising trajectory) alters the previously estimated demand rhythms.
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