End-use allocation

In this section the models and techniques used by the UK statistical office to estimate the allocation of Final energy to the different end-uses are described. In light of this information, the ucertainty parameter for these allocations is defined for each sector.

Residential

The quantification of the energy used for each end-use application within dwellings is provided by the Cambridge Housing Model [64]. It is a bottom-up, physical, housing energy consumption model based on Standard Assessment Procedure 2009 calculations – the government agreed procedures to establish compliance with building regulations. The model uses data from the English Housing Survey [65]: a stratified random sample of around 15 000 English dwellings conducted annually. Each building in the survey is modelled and its energy consumption estimated, the results are then scaled and weighted by dwelling type and dwelling age, to represent national level values. The end-use applications modelled are:

CD Estimate (θ_f,s,e,d) Estimated (Table 5) ECUK (F_f,s,e)

Estimated (Table 4) Energy Balance (F_f,s)

GHG Inventory

Normalise (φ_f,s)

Lognormal/Normal Dirichlet Dirichlet

Monte Carlo Multipl.

Ff,s x φ^ef,s x θ^df,s,e

η Estimate (ηf,s,e,d) Estimated (SI) F_s,f,e,d

Calculated

Monte Carlo Multipl.

Fs,f,e,d x ηf,s,e,d

U_s,f,e,d

Normal/Uniform/Triang.

Calculated

Legend

Central Value Uncertainty

Fig. 3.2 Flowchart summarising the steps taken to calculate the Useful energy demand of the UK and its uncertainty, from data sources to results. The text shaded in grey refers to the uncertainty of the data, described in the transparent boxes. The text in brackets indicates where the assumptions are found in the text. The abbreviations used are the following.

ECUK:Energy Consumption UK, CD: Conversion Device, SI: Supporting Information.

Space Heating - Main, Space Heating - Secondary, Water Heating, Space Cooling, Lighting, Electrical Appliances, Cooking, Pumps and Fans.

In addition, for the consumption of electricity in UK dwelling a better data source is avail-able. In 2010 a measurement campaign was conducted with the aim of understanding how electricity is used in UK homes. Electricity meters were installed in a stratified sample of 251 homes [55]. Electricity use is classified in the following categories: Cold Appliances, Cooking, Lighting, Audiovisual, ICT, Washing/Drying, Heating, Water Heating, Other, Not known. The uncertainty in the estimate is due to a share of "not-known" energy consumption, a sampling error (about 6%) and a measurement error (about 2%).

Two uncertainty and sensitivity analyses have been performed on the CHM model by its authors [255, 256]. The authors estimate the uncertainty in the model’s input parameters, which they define using expert judgement and estimates found in the literature. A uniform probability distribution is used with values ranging from 1% for wind speed to 50% for wall U-values. The input uncertainties are propagated using a Monte Carlo simulation. The result of the analysis is only published for the entire energy demand of the UK residential sector.

In the 2016 version of the ECUK data [257], there is a note about the end-use results which states that

The breakdown of energy by final use is based on modelling, and this is subject to uncertainty from housing data, behavioural data, climate data and building physics assumptions. The proportions used in the breakdown could vary by as much as 18%.

This text is interpreted as a relative uncertainty of 18% on the largest share of each allocation vector. This interpretation is very conservative since the resulting uncertainties for other end-uses will be higher than the 18% specified. This estimate does not take into account the higher resolution data available for electricity consumption as there is no metion of it in the published end-use statistics.

Commercial Services and Public

The allocation of energy consumption to the various end-use allocations in the commercial and public sector is done with the results of the Building Energy Efficiency Survey [66]. The survey is based on 4084 telephone interviews and 284 site visits of non-residential buildings sampled across the UK. The model estimates energy consumption by associating each area of the building to a "space type" which in turn is linked to a specific end-use consumption profile.

Information about the building’s space type and efficiency of equipment is determined in the phone interviews [67].

The results of the model have been peer reviewed by a group of researchers at UCL [258].

They compared the output for the BEES model to their own, in-house building energy model.

The allocation of energy to end-uses was checked by running both models on two samples of office spaces. However, no formal uncertainty analysis was performed on this model.

In the literature, there are estimates of uncertainties associated with non-residential building simulations in the UK [259–261], but no estimate of the uncertainty for the end-uses of energy in the buildings was found since most studies focus on the uncertainty associated with the total energy consumption of buildings and even then uncertainty analyses are rare [262].

For this study, the uncertainty of end-uses in the service and commercial sectors is assumed to be 20% for the largest share. This is in line with the differences observed between the BEES model and the UCL model for office spaces [258]

Industry

The industrial breakdown is based on the non-domestic Energy and Emissions Model, which uses a survey last conducted in 2000 [263].

Industrial end-uses of energy are provided in the following categories: Drying/Separation, Motors, Compressed Air, Lighting, Refrigeration, Space Heating, Other. Very few details are known about the surveys and methodologies used to determine these end-use application of energy in the industrial sector in the UK. One recent study by the Fraunhofer ISI [264]

aimed at quantifying the energy used for heating and cooling in the EU industry, quotes an uncertainty value of 25% for its estimates which has been agreed upon by various practitioners.

This will be used as the uncertainty of the largest share for the industrial allocation vector.

Agriculture, Fishing and Forestry

The data for the end-use allocation in Agriculture is extracted from a report by the University of Warwick [265]. The allocation is not based on a survey, but rather on expert judgement.

An assessment of the uncertainty in these estimates would require a formal interview with the experts that conducted the study. Such efforts are not warranted by the low share of energy consumed in this sector. An uncertainty of 25%, as for industry, is assumed.

Table 3.5 summarises the uncertainty value associated with each end-use allocation used in this study.

Table 3.5 Summary of the uncertainty associated with each end-use allocation vector esti-mated by the author. Further details about the rationale underpinning the values is found in Section 1 of the Supporting Information

Sector Uncertainty

Residential 18%

Service and Commercial 20%

Industrial 25%

Agriculture, Forestry, and Fishing 25%