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Understanding the complexities of domestic energy reductions in cities: Integrating data sets generally available in the United Kingdom's local authorities

Lookup NU author(s): Javier Urquizo Calderon, Dr Carlos Calderon, Professor Philip James

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

This paper describes the development of a spatial database for an annual energy consumption framework. As the database of the existing building stock is a weak point in most European cities but possible pathways to energy reduction in the building sector have to be found, this paper is of high relevance. Our framework uses a complex multi-source data set and a plethora of statistical methods to merge various large complex data sets and then apply a heat balance model in three sub-city areas in Newcastle upon Tyne, United Kingdom. The framework estimates the energy end-use at the single dwelling level on three aggregated scales: district, neighbourhood and communities. We propose a methodology for modelling energy in buildings in different ways depending on the required output scale, the cluster top-down model and the domestic energy model (DEM) bottom-up approaches. The cluster model is a generalization of similar building energy profiles into archetype medoid prototypes in districts and eventually the whole city, whereas the sub-city DEM is a representation of an individual building energy pro- file in neighbourhoods and communities. The framework can be used to test different energy measures (fabric and heating supply systems) for the same property type, and give insights into community energy analysis by aggregating individual building energy consumption. These insights will enable rational and considered responses to be formulated to the problems of integrating renewables into the generation portfolio, that are likely to be faced in the future.


Publication metadata

Author(s): Urquizo J, Calderón C, James P

Publication type: Article

Publication status: Published

Journal: Cities

Year: 2018

Volume: 74

Pages: 292-309

Print publication date: 01/04/2018

Online publication date: 30/12/2017

Acceptance date: 22/12/2017

Date deposited: 05/01/2018

ISSN (print): 0264-2751

Publisher: Elsevier

URL: https://doi.org/10.1016/j.cities.2017.12.019

DOI: 10.1016/j.cities.2017.12.019


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