Energy used in the operation of the United Kingdom’s non-domestic buildings contributes 18% of national carbon dioxide emissions and reducing these is government policy. The use of electrical equipment in buildings is a major contributor to overall consumption, due to both its intrinsic energy consumption and the effects of incidental internal gains resulting from its operation. Knowledge of how and where consumption and internal gains occur in buildings is important in understanding the consumption characteristics of the building stock. The overall aim of this research was to improve the prediction of energy consumption in the non-domestic stock through the inference of appliance electricity consumption and resultant heat gains, for internal space uses of premises, as identified in UK property taxation data. To achieve this, the objectives were to: 1. Develop a method for inferring space usage in premises. 2. Infer values for the electricity consumption of appliances, and hence internal gains, for space uses within premises. 3. Apply the method to a dataset at the urban scale and use a suitable model to deduce the energy consumption. 4. Compare the results with measured data. Objectives 1 and 2 were achieved through analyses of detailed energy surveys of more than 300 non-domestic premises. By excluding equipment used for heating and cooling, both intrinsic electricity consumption and internal gains from appliances have been characterised for combinations of internal space use and premises activity type. For each combination, the characteristics include the energy intensity (kWh/m2/year) for: • overall appliance use • 14 end uses of appliances (e.g. lighting, catering, computers) • 18 groups of appliance activity descriptions (e.g. sales, office work, process) These characteristics were mapped onto subdivisions of space use, within premises, listed in property taxation data for a test urban area (City of Leicester). Using only 115 descriptions of space use, appliance consumption characteristics have been inferred for 91.5% of the measured internal floor area of the test dataset; this achieved the third objective. More than 80% of the floor area was identified using standard space use descriptions utilised in real estate taxation datasets. The total estimated consumption accounted for 75% of the recorded annual electricity consumption of the test area (the fourth objective). This result is acceptable, given the known limitations of the datasets and suggests that the method constitutes an improvement to stock energy modelling, thus meeting the overall aim. By inferring appliance electricity consumption and internal gains at a finer spatial resolution than previous methods, the diversity of energy consumption characteristics of the non-domestic stock may be represented more faithfully than by values applied to entire homogenised premises or premises types. The method may be used by policy makers as part of an urban energy model and as a means of evaluating potential energy interventions in the non-domestic stock, or parts thereof.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:571306 |
| Date | January 2012 |
| Creators | Liddiard, Robert |
| Publisher | De Montfort University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2086/6105 |
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