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1.4. Methodology
The two main sources of data used in the analysis were energy supplier annualised meter point gas and electricity data and the Homes Energy Efficiency Database (HEED). The gas and electricity meter point data was provided by the Department of Energy and Climate Change (DECC) and covered the period of 2004 through 2007. The gas and electricity meter point values were derived from inpidual meter readings, via data aggregators of the gas and electricity suppliers. Access to HEED was also provided by DECC through the Energy Saving Trust (EST). The next section contains a detailed description of the two data sources and a description of the analysis methods.
2. Data
2.1. Gas and electricity meter data
The government collects annualised final consumption gas and electricity data for inpidual meter points from energy suppliers for the purpose of various statistical outputs; in 2007 there were approximately 22.6 million gas meters (22.3 million residential and 0.3 million non-domestic) and 29.1 million electricity meters (26.7 million residential meters and 2.4 million non-domestic meters) (DECC, 2009b). UK gas and electricity meters are classified into two types: daily (gas) or half-hourly (electricity) metered, and non-daily (gas) or non-half hourly (electricity) metered. The non-half hourly and non-daily meter data was linked to HEED by Government for use in this project. Between 2004 and 2008, gas and electricity accounted for just over 90% of total fuel delivered to UK dwellings (DECC, 2012b).
Gas non-daily meters are pided into categories based on their total expected annual load demand; gas meters contain no user identification and ‘residential’ users are determined to be those whose demand was less than 73.2 MWh/yr and those above are commercial or industrial (DECC, 2009b). Meter readings are converted into annual consumption values by the suppliers using a common methodology with two meter readings at least 6 months apart (when no meter reading is available an estimate based on past demand is used in its place) and is corrected to a seasonal normal demand and an end-user climate sensitivity adjustment to derive a total annual demand (OFGEM, 2013). The purpose of the seasonal correction is to allow for inter-year comparisons that are independent of weather. In terms of what the weather correction might mean for assessing the impact of energy efficiency interventions through the detection of changes in energy demand between years, it may be that long-term trends are more significant than year-on-year changes, but this will depend on the frequency of meter readings for which no information is available. The gas data annual period is 1 October to 30 September and covers a heating season.
Electricity non-half hourly meters are defined into classes representing likely demand profiles and contains a user type identifier. Residential electricity meters are classed into two types based on the meter, i.e. unrestricted electricity or Economy 7. Economy 7 refers to meters that are on a time charge tariff offering cheaper electricity during off-peak hours, typically an 8 h period, and are either time or radio switched (DECC, 2009b); in dwellings, these meters are most often associated with electric heating, either space heating (e.g. storage heaters) or hot water, offering the customer the advantage of electricity bought at off-peak rates and stored as heat for daytime use; in this work Economy 7 m were kept as distinct. Unrestricted meters are all other types of meters; these meters may be used for heating but are not time or radio switched. Electricity meters are annualised using actual meter readings or, if no readings are available estimates based on past use and historic usage patterns and are smoothed across an annual profile to derive a total annual demand in kWh (Elexon, 2010). The annualised electricity values are not corrected for weather. The electricity data annual period is from 30 January to 29 January.
Both the gas and electricity data underwent a cleaning process to remove or identify potentially erroneous data points, such as negatives and dummy values (e.g. ‘1’ values). In this paper, a dataset that removed erroneous data points was used in all energy analysis.