Open AccessSpatial mapping of building energy demand in G reat B ritain
Author(s) -
Taylor Simon C.,
Firth Steven K.,
Wang Chao,
Allinson David,
Quddus Mohammed,
Smith Pete
Publication year - 2014
Publication title -
gcb bioenergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.378
H-Index - 63
eISSN - 1757-1707
pISSN - 1757-1693
DOI - 10.1111/gcbb.12165
Subject(s) - electricity , environmental science , bioenergy , energy consumption , spatial variability , environmental economics , range (aeronautics) , spatial analysis , renewable energy , geography , economics , engineering , statistics , mathematics , remote sensing , aerospace engineering , electrical engineering
Maps of energy demand from buildings in G reat B ritain have been created at 1 km square resolution. They reveal the spatial variation of demand for heat and electricity, of importance for energy distribution studies and particularly for bioenergy research given the significant distance‐based restrictions on the viability of bioenergy crops. Maps representing the spatial variation of energy demand for the year 2009 were created using publicly available sub‐national gas and electricity consumption data. A new statistical model based on census data was used to increase the spatial resolution. The energy demand was split into thermal energy (the heat energy required for space heating and hot water) and electricity used for purposes other than heating (nonheating electricity or NHE ) and was determined separately for the domestic and nondomestic sectors. ‘Scenario factors,’ representing the fractional change at national level in the demand for heat and NHE , were derived from scenarios constructed by UKERC . These scenarios represent a range of pathways from the present day to 2050. The present work focused on the two cases of greatest relevance, the ‘low carbon’ and ‘additional policies’ scenarios, and factors for both were derived, for the demand types described, for every 5 years between 2000 and 2050. Approximately, future spatial energy demands can be obtained by applying the scenario factors to the base mapping data for 2009.