Electricity consumption and household characteristics: Implications for census-taking in a smart metered future
Electricity consumption and household characteristics: Implications for census-taking in a smart metered future
复制标题
DOI：
10.1016/j.compenvurbsys.2016.06.003
复制
发表时间：
2017-05-01
影响因子：
6.8
通讯作者：
James, Patrick
中科院分区：
文献类型：
--
作者：
Anderson, Ben;Lin, Sharon;James, Patrick
本文评估的可行性，确定关键的家庭特征的基础上，家庭电力需求的时间负荷曲线。众所周知，家庭特征、行为和惯例以目前尚未完全理解的方式驱动家庭电力负载的许多特征。在英国和其他地方推出的家用智能电表可以通过在家庭层面收集高时间分辨率的电力监测数据来更好地理解。这些数据提供了巨大的潜力，以扭转家庭特征和时间负荷分布之间的既定关系。与其使用住户特征作为负荷预测指标，不如使用观察到的电力负荷概况或以此为基础的指标来估算住户特征。这些微观一级的估算特征可以在小地区一级汇总，以产生“类似普查”的小地区指标。这项工作简要回顾了目前和未来的人口普查在英国的性质，然后概述了家庭的特点，在英国人口普查中发现，也被称为影响电力负荷配置文件。然后，它提出了一个大规模的智能电表类数据集的半小时家庭用电量的描述性分析，然后审查家庭属性和电力负荷配置文件之间的相关性。然后，本文报告了这些关系的多层次模型为基础的分析结果。这项工作的结论是，一些家庭的特点，可以发现在英国人口普查派生的小面积的统计数据，从特定的负荷分布指标预测。还讨论了测试和验证这种方法所需的步骤以及对普查的更广泛影响。(C)2016作者出版社：Elsevier Ltd
This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce 'census-like' small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of a large scale smart meter-like dataset of half-hourly domestic electricity consumption before reviewing the correlation between household attributes and electricity load profiles. The paper then reports the results of multilevel model-based analysis of these relationships. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided. (C) 2016 The Authors. Published by Elsevier Ltd.