Smart water metering as a non-invasive tool to infer dwelling type and occupancy - Implications for the collection of neighbourhood-level housing and tourism statistics
Smart water metering as a non-invasive tool to infer dwelling type and occupancy - Implications for the collection of neighbourhood-level housing and tourism statistics
复制标题
智能水表作为推断住宅类型和占用率的非侵入性工具 - 对收集邻里住房和旅游统计数据的影响
DOI：
10.1016/j.compenvurbsys.2023.102028
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Newing A
中科院分区：
文献类型：
--
作者：
Newing A
住宅供水部门先进计量基础设施(AMI)的国际推广在提高用水效率、准确计费和网络管理(如泄漏检测)方面带来了巨大的好处。阿米使用安装在住宅水平上的智能电表，以高时间分辨率记录用水量。由于水通常只在住户在场的情况下才会被消耗，这些数据可以提供一种非侵入性的手段来推断住宅的居住模式。这些洞察力可能会产生一系列好处，具体取决于时空尺度和预期的应用--我们感兴趣的是这些数据识别住宅类型的可能性，特别是识别具有与旅游业相关的居住模式的住宅，如第二套住房或短期度假租赁。我们专注于英国背景下的这些数据，并利用了学术研究中很少获得的数据。我们的数据涉及英格兰西南部德文郡和康沃尔的住宅样本。它们捕捉到了新冠肺炎“封锁”和“闲置”期间的高时间分辨率用水量，提供了一个独特的机会来展示这些数据可以揭示这一时期不同寻常的显著物业占用趋势。我们应用非侵入性入住率监测(NIOM)来逐日提取住宅级别的入住率状态(已占用/未占用)。我们根据它们的入住率趋势对房产进行分组，推断出一组与旅游业相关的入住率特征的房产。我们证明，这些指标与旅游活动的基本指标相符，这些指标来自AirDNA对该地区短期游客租赁物业的记录。正在进行的AMI全球推广意味着这些数据将在住宅层面上定期可用，我们反思了它们在生成对住宅入住率的近乎实时的洞察方面所能提供的好处。根据我们与国家统计局(英国国家统计局)的合作，我们概述了这些数据和方法在整理小区域住房和旅游业统计数据方面的巨大潜力。
The international rollout of advanced metering infrastructure (AMI) in the residential water supply sector affords tremendous benefits in driving water-use efficiencies, accurate billing and network management (e.g. leak detection). AMI, using ‘smart meters’ fitted at a dwelling level, record water consumption at high temporal resolution. Since water is typically only consumed when householders are present, these data could offer a non-intrusive means of inferring dwelling occupancy patterns. These insights could have a range of benefits dependent upon the spatiotemporal scale and the intended application – our interest is in the potential of these data to identify dwelling type, specifically to identify dwellings that have occupancy patterns associated with tourism, such as second homes or short-term holiday rentals. We focus on these data in a UK context and draw on data rarely available for academic research. Our data relate to a sample of dwellings in Devon and Cornwall, South West England. They capture high-temporal resolution water consumption during Covid-19 ‘lockdown’ and ‘staycation’ periods, providing a unique opportunity to demonstrate that these data can reveal the unusually pronounced property-level occupancy trends evident during this period. We apply Non-Intrusive Occupancy Monitoring (NIOM) to extract dwelling-level occupancy status (occupied/unoccupied) on a day-by-day basis. We group properties according to their occupancy trends, inferring a set of properties that exhibit occupancy characteristics associated with tourism. We demonstrate that these show correspondence with underlying indicators of tourism activity, drawn from AirDNA records of short-term tourist rental properties in this area. Ongoing global rollout of AMI means that these data will be routinely available at the dwelling level and we reflect on the benefits they could provide in generating near real time insights into dwelling occupancy. Drawing on our collaboration with the Office for National Statistics (the UKs national statistical institute) we outline the considerable potential that these data and approaches could offer in the collation of small area housing and tourism statistics.
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影响因子：
3.4
作者：
Tracy Clare Britton;G. Cole;R. Stewart;D. Wiskar
通讯作者：
Tracy Clare Britton;G. Cole;R. Stewart;D. Wiskar
西班牙家庭智能水表：阿利坎特远程抄表经验的启示
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
H. March;Álvaro;A. Rico;D. Saurí
通讯作者：
D. Saurí
智能水表
DOI：
--
发表时间：
2019
期刊：
影响因子：
--
作者：
Adedamola A. Omotoso;S. Musa;M. Sadiku
通讯作者：
M. Sadiku
DOI：
10.1016/j.proeng.2014.11.216
发表时间：
2014
期刊：
Procedia Engineering
影响因子：
--
作者：
A. Sønderlund;Joanne R Smith;C. Hutton;Z. Kapelan
通讯作者：
A. Sønderlund;Joanne R Smith;C. Hutton;Z. Kapelan
模拟水需求管理方案对水消耗和废水产生情况的影响
影响因子：
2.7
作者：
S. Sadr;Line T. That;W. Ingram;F. Memon
通讯作者：
F. Memon