Abstract

Empirical domestic energy demand data can be difficult to obtain, due to a combination of monitoring, data access/ownership and cost issues. As a result, it is quite common to see domestic energy assessments based on modelled energy consumption. When looking at quite specific metrics of energy consumption, such as minutely domestic electrical demands, the data that does exist tends to be for a relatively small number of homes. The methods presented here provide a starting point for extrapolating this information so that such data can be used to represent a much larger group of homes, and therefore have wider applications. While limitations still exist for the extent of this extrapolation, issues such as diversity of demand and occupancy variations can be accommodated within an appropriate statistical analysis. The method also demonstrates that, by using the synthesis method to characterise the patterns within a daily domestic demand pattern, informed estimations can be with regards to the type of activity being carried out within the dwelling. Synthesised aggregated datasets (representing a larger group of dwellings) are also compared to real demand profiles from substations, to investigate whether similar patterns are being observed. This is part of the Adaptation and Resilience in Energy Systems (ARIES) project, looking at energy demand and supply in a future climate. (c) 2014 The Authors. Published by Elsevier B.V.

Original languageEnglish
Pages (from-to)605-614
Number of pages10
JournalEnergy and Buildings
Volume76
DOIs
Publication statusPublished - Jun 2014

Keywords

  • Electrical demand
  • ADMD
  • Domestic energy consumption
  • LOADS
  • MODEL

Cite this

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title = "Synthesising electrical demand profiles for UK dwellings",
abstract = "Empirical domestic energy demand data can be difficult to obtain, due to a combination of monitoring, data access/ownership and cost issues. As a result, it is quite common to see domestic energy assessments based on modelled energy consumption. When looking at quite specific metrics of energy consumption, such as minutely domestic electrical demands, the data that does exist tends to be for a relatively small number of homes. The methods presented here provide a starting point for extrapolating this information so that such data can be used to represent a much larger group of homes, and therefore have wider applications. While limitations still exist for the extent of this extrapolation, issues such as diversity of demand and occupancy variations can be accommodated within an appropriate statistical analysis. The method also demonstrates that, by using the synthesis method to characterise the patterns within a daily domestic demand pattern, informed estimations can be with regards to the type of activity being carried out within the dwelling. Synthesised aggregated datasets (representing a larger group of dwellings) are also compared to real demand profiles from substations, to investigate whether similar patterns are being observed. This is part of the Adaptation and Resilience in Energy Systems (ARIES) project, looking at energy demand and supply in a future climate. (c) 2014 The Authors. Published by Elsevier B.V.",
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Synthesising electrical demand profiles for UK dwellings. / Jenkins, David P; Patidar, Sandhya; Simpson, Sophie Ann.

In: Energy and Buildings, Vol. 76, 06.2014, p. 605-614.

Research output: Contribution to journalArticle

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AB - Empirical domestic energy demand data can be difficult to obtain, due to a combination of monitoring, data access/ownership and cost issues. As a result, it is quite common to see domestic energy assessments based on modelled energy consumption. When looking at quite specific metrics of energy consumption, such as minutely domestic electrical demands, the data that does exist tends to be for a relatively small number of homes. The methods presented here provide a starting point for extrapolating this information so that such data can be used to represent a much larger group of homes, and therefore have wider applications. While limitations still exist for the extent of this extrapolation, issues such as diversity of demand and occupancy variations can be accommodated within an appropriate statistical analysis. The method also demonstrates that, by using the synthesis method to characterise the patterns within a daily domestic demand pattern, informed estimations can be with regards to the type of activity being carried out within the dwelling. Synthesised aggregated datasets (representing a larger group of dwellings) are also compared to real demand profiles from substations, to investigate whether similar patterns are being observed. This is part of the Adaptation and Resilience in Energy Systems (ARIES) project, looking at energy demand and supply in a future climate. (c) 2014 The Authors. Published by Elsevier B.V.

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