El rol de las agencias

In the monitoring studies reviewed so far, the predominant focus of data collection has been the measurement of household appliance electricity consumption. A number of more recent monitoring campaigns have taken a similar approach, but have taken a greater interest in householder behaviour.

In New Zealand, the Household Energy End-use Project (HEEP) collected a variety of data from 398 homes, which including energy use, temperature, appliances, and hot water use between 1997 and 2005 (Isaacs, Camilleri, French, Pollard, Saville-Smith, Fraser,

Rossouw and Jowett, 2006a). Additional occupant data were collected through a survey at the installation phase. Appliances in one-hundred homes were monitored, at the appliance level, with one to three appliances monitored each month. Due to limitations in available monitoring equipment data for some appliances was limited or not recorded at all (Isaacs et al., 2006a).

Some of the results from the research are difficult to compare to the UK, due to 75% of New Zealand‟s domestic electricity consumption being attributable to water heating, but a general pattern of increased ICE appliance use was reported and 51% of standby

consumption was from entertainment appliances (Isaacs et al., 2006a). Furthermore, the results from HEEP highlight that the collection of “real world” data is essential to

understand domestic electricity consumption. In a conference paper, the authors describe that “real data can challenge conventional thinking and even result in changes to official statistics” (Isaacs, Camilleri and French, 2006b p10) and conclude that “market surveys and thermal models based on “conventional knowledge” are no substitute for monitored data” (Isaacs et al., 2006b p10). The reason for this is the importance of householder behaviour. The authors argue that:

The interaction between the house, energy-using appliances and occupant behaviour is so complex that it is simply not possible to predict energy use. Thermal simulation models need data of good quality and accuracy in order to give valid predictions and that data just has to be collected – there is no other reliable way to get it. Often the most important

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determinants of energy use are behavioural, and no physical model can provide the details.

(Isaacs et al., 2006b p10)

Conclusions from the HEEP study also highlight the validity of understanding extremes of household electricity consumption. Isaacs et al. (2006b) argue that many research studies have been focused on the application of statistical analysis to derive average electricity consumption values, but this raises the question; “are the extreme values statistical anomalies (and therefore should be excluded from a robust analysis) or are they realistic reflections of the huge spread of energy use” (Isaacs et al., 2006b p10).

Isaacs et al. (2006b) also believe that such extremes “are not measurement outliers – they may only occur in a few houses, but they are real cases that cannot be dismissed” (Isaacs et al., 2006b p10). This belief suggests that the detailed evaluation of real cases of electricity consumption can add to current understanding of energy use, even though the average consumption values for households and appliances are less meaningful in terms of a larger population. This is important for the validity of this thesis work when it is considered that the sample size is relatively small.

A further conclusion from the HEEP study is apparent through a recent overview of the study by Camilleri (2009). He states that “HEEP answered the questions of „what‟ and

„how‟ energy is used, but did not do well with the „why‟” (Camilleri, 2009 p7). A potential reason for this is reflected in the statement that the occupant survey “should have been developed more than it was [and that] early and ongoing participation of a social scientist and statistician are vital” (Camilleri, 2009 p7). This suggests that in order to better understanding domestic electricity consumption it is necessary to also investigate the context and underlying motivations for the measurements recorded.

Research by Firth, Lomas, Wright and Wall (2008), as part of the CaRB project, supports this position. The Firth et al. (2008) study investigated domestic appliance electricity consumption by analysing data from five-minutely average whole house power

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consumption measurements that were recorded, over a two year period, for seventy-two dwellings at five sites in the UK. However, no householder survey was undertaken.

Techniques were developed to estimate appliance electricity consumption from three appliance groups; (i) cold appliance consumption; (ii) active appliance consumption; (iii) continuous and standby consumption (Firth et al., 2008).

The Firth et al. (2008) results show a large variation in the annual electricity consumption, even in sites with a similar size or built form. This suggests that in the UK “built form is not a strong determining factor in household electricity use” (Firth et al., 2008 p935). Instead it appears that factors such as household size, number and type of appliance and patterns of use are more relevant. When compared, year one and year two results show an

increase, on average, of 10.2% for continuous and standby appliances and 4.9% for active appliances. Cold appliances were shown to have decreased by 1.5%. This reflects the UK trend of increasing ICE appliance use and the improved efficiency of cold appliances.

High and low energy using householders were responsible for the overall increase in electricity consumption, which was through the increased electricity consumption of continuous and standby appliances and active appliances (Firth et al., 2008).

The study establishes the role of monitoring to better understand the trends in dwellings electricity consumption, but the work found that active appliances (which includes many ICE appliances) were difficult to identify and “there were often no discernable pattern of use” (Firth et al., 2008 p932). This suggests that more detailed monitoring, at the appliance level, is necessary to investigate the factors for household electricity consumption. Importantly, Firth et al. (2008) argue that:

Only by linking measured data, such as that used in this paper, with quantitative surveys of appliance ownership can greater insight can be gained and only by linking such studies with qualitative social science research to understand the motivations and drivers for appliance usage, can policies for reducing consumption can be reliably framed.

(Firth et al., 2008 p935)

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The MTP (2006b) investigation into home computer use is another study that recommends the use of qualitative data collection. The study provided the Lot 3 EuP preparatory study with some of the best available data for household computer use (IVFIRDC, 2007). The research aimed to establish the average length that computers were used in different power modes, determine the number of computers with power management features and examine patterns of behaviour (MTP, 2006b). The methodology applied to the research included the monitoring of the main computer unit with unobtrusive electrical power data loggers, for a period of two weeks, at one minutely intervals (MTP, 2006b).

Questionnaires were administered, by an interviewer, to collect information concerning the following:

 Computer type and specification;

 Age of computer and general patterns of use;

 Socio-demographics;

 Power management functions (in some cases the interviewer accessed the computer control panel).

The sample consisted of eighty households across the UK, recruited from ten regions within England, in order to be as representative as possible. The research provided average patterns of use and electricity consumption data for computer base units and identified that despite 95% of the computers having power management features most of the householders did not use them (MTP, 2006b). For example, 86% of the computers could activate “system standby”, but only 22% operated with this function (MTP, 2006b).

Also, 60% of respondents used the computers for work related activities and 80% for other activities, which suggests that computer use is linked to more flexible working patterns and is more integrated into daily activities (MTP, 2006b).

Despite the research providing data concerning user behaviour, the report acknowledges that it would be “very valuable to investigate this behaviour further” (MTP, 2006b p23).

The report recommends that qualitative research could provide answers to questions, such as whether computer users are unaware of power management features and why they are not taking advantage of them.

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Two recent large-scale monitoring campaigns that have collected ICE appliance electricity consumption data are the REMODECE project and an electricity end-use campaign

conducted by the Swedish Energy Agency (De Almeida et al., 2008; Bennich and Persson, 2006). In addition to the collection of electricity consumption measurements, these studies also included the collection of social data. Therefore, these studies have been included in chapter 4.

2.4 Summary

This chapter has provided an overview of literature regarding current and future predictions of household ICE appliance use and some of the key policy issues in this sector. There is only limited contemporary “real world” ICE appliance data concerning UK households‟ appliance usage patterns and standby power consumption. This deficit in knowledge is restricting UK modelling and forecasting, and the implementation of effective policy measures. The review of domestic electricity monitoring campaigns has shown that the provision of accurate usage patterns can help to fill this gap in knowledge and even exploratory studies have the potential to reveal new and potentially significant forms of domestic electricity consumption.

Importantly, the literature review identified that electricity consumption monitoring studies have not always been able to answer why measurements recorded in homes occurred.

Studies have concluded that the inclusion of social science research methods, to investigate householders‟ behaviour, would provide more comprehensive results from future research. Chapter 4 will return to this argument through a review of a number of studies that have included both technical and social science research methods. However, Chapter 3 first reviews a body of social science research that has shown household behaviour to be a significant factor for variance in household energy consumption.

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Chapter 3. Literature Review 2: Energy consumption and