Deconstruct: A scalable method of as-built heat power loss coefficient inference for UK dwellings using smart meter data

被引:24
作者
Chambers, Jonathan D. [1 ,2 ]
Oreszczyn, Tadj [3 ]
机构
[1] Univ Geneva, Chair Energy Efficiency, Inst Environm Sci, Blvd Carl Vogt 66, CH-1205 Geneva, Switzerland
[2] Univ Geneva, Dept FA Forel Environm & Aquat Sci, Blvd Carl Vogt 66, CH-1205 Geneva, Switzerland
[3] UCL, UCL Energy Inst, Cent House,14 Upper Woburn Pl, London WC1H 0NN, England
基金
英国工程与自然科学研究理事会;
关键词
Energy demand; Residential sector; Smart meter; Building assessment methods; Building energy models; ENERGY EFFICIENCY; PERFORMANCE EVALUATION; BUILDING PERFORMANCE; CHALLENGES; PROFILES; SAVINGS;
D O I
10.1016/j.enbuild.2018.11.016
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Dwellings in the UK account for about 25% of global energy demand, of which 60% is space heating making this a key area for efficiency improvement. Dwelling UK Energy Performance Certificates (EPC) are currently based on surveyed data, rather than energy use monitoring. The installation of smart meters provides an opportunity to develop an EPC based on in situ dwelling thermal performance. This paper presents 'Deconstruct' - a method of estimating the as-built Heat Power Loss Coefficient (HPLC) of occupied dwellings as a measure of thermal performance, using just smart-meter and meteorological data. Deconstruct is a steady-state grey box building model combined with a data processing pipeline and a model fitting method that limits the effects of confounding factors. Smart meter data from 780 UK dwellings from the UK Energy Demand Research Project (EDRP), was used to calculate a median HPLC of 0.28 kW/degrees C (+/- 15%). The stability of the estimate across multiple years of data with different weather and energy use was demonstrated. Deconstruct was found to be suitable for large scale inference of dwelling thermal properties using the UK's new smart metering data infrastructure. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:443 / 453
页数:11
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