by K.T. Weaver, SkyVision Solutions
As recently stated by Lee Tien, senior staff attorney for the Electronic Frontier Foundation, “Few other types of data get inside the home the way that electrical usage data does.” Governments and corporations everywhere are recognizing this fact as more and more smart meters are deployed into the population.
A new paper has just been published entitled, “The Role of Digital Trace Data in Supporting the Collection of Population Statistics – the Case for Smart Metered Electricity Consumption Data.” This paper demonstrates how technocrats are continually finding innovative ways to use smart meter ‘big data’ while invading your privacy in the process. As stated in the paper’s conclusion:
“Overall, our review of evidence to date, combined with preliminary descriptive analysis, has suggested that there is value in exploring the use of smart meter electricity consumption data for the purposes of deriving population statistics and indicators of household attributes at small area levels of geography.”
“With electricity smart meter roll out underway or anticipated in a number of the largest domestic markets including the US, China, Brazil, India, and Japan, alongside France, Germany, and Spain, we suggest that there is considerable potential for this form of analysis in a variety of national contexts. In the UK, this form of data could supplement official census-type statistics, addressing users’ requirements for timely reporting of small area statistics.”
As an example, and more specifically, by reviewing your household granular energy load profile collected by a smart meter, researchers are developing algorithms to determine home occupancy including the number of adults and children in the home. As explained in the paper and as shown in the above figure:
“Figure 2 presents temporal load profiles for 87 of the study households grouped by the number of residents and their broad age group. … The midweek period (Tuesday–Thursday) is representative of weekday routines, and whilst the night-time base load appears consistent across all households, it is clear that larger households exhibit a higher magnitude daytime load, particularly during the evening peak period, with households comprising two adults and three children exhibiting a higher maximum mean load (1.31kW) than smaller one adult households (0.57 kW). Thus, electricity loads during the peak period appear to represent an indicator of household size.”
“Furthermore, households where children are present show more pronounced morning peak demand, potentially driven by routines associated with preparation for school. Even if the underlying cause of these observed behaviours are uncertain, Figure 2 suggests that key household compositional indicators such as the number of household residents or the presence of children could be inferred from temporal load profiles.”
Researchers are also working on determining other “population statistics” such as employment status, income, and energy inequality by reviewing smart meter data and making use of the following additional research findings:
“After accounting for household composition, it is apparent that there is a general trend towards higher mean loads by higher income households especially during the early evening peak period. … Thus, there is potential that smart metered electricity consumption data of this nature could afford value as a potential indicator of household income, especially if underlying confounding characteristics, such as household composition, are known,…”
“Consideration of the seasonal dimension may also reveal behaviours associated with use of primary or secondary electric space-heating. As we have already noted, use of timers or thermostatic controls to operate these devices may mask some of the energy using behaviours that can be used to identify household characteristics, but may also provide a valuable insight into energy inequality, enabling novel policy-relevant indicators to be developed.”
The last sentence in the paper states the following:
“The potential for additional value extraction from smart meter data through an aggregated data analytics market, whilst maintaining household level non-disclosure and privacy, is clear.”
The word privacy only occurs one time in the paper as indicated above. Almost as an afterthought, the authors evidently thought they should include that word, and I hope you realize the irony. The entire research project and paper is about invading privacy by analyzing smart meter granular data and load profiles and revealing whatever information can be discerned about behaviors, details, and “statistics” within the home. Your privacy is lost at the moment of granular data collection, no matter whatever “non-disclosure” policy a corporation or government may have in place.
Primary Source Material for this Article
“The Role of Digital Trace Data in Supporting the Collection of Population Statistics – the Case for Smart Metered Electricity Consumption Data,” by Andy Newing, Ben Anderson, AbuBakr Bahaj, and Patrick James (2015); Population Space and Place; article first published online: 20 July 2015; refer to http://www.energy.soton.ac.uk/census2022-on-smart-meter-data-for-a-smart-census-published/; also
“Debates over the future of the UK’s traditional decadal census have led to the exploration of supplementary data sources, which could support the provision of timely and enhanced statistics on population and housing in small areas. This paper reviews the potential value of a number of commercial datasets before focusing on high temporal resolution household electricity load data collected via smart metering.
We suggest that such data could provide indicators of household characteristics that could then be aggregated at the census output area level to generate more frequent official small area statistics. These could directly supplement existing census indicators or even enable development of novel small area indicators.
The paper explores this potential through preliminary analysis of a ‘smart meter-like’ dataset, and when set alongside the limited literature to date, the results suggest that aggregated household load profiles may reveal key household and householder characteristics of interest to census users and national statistical organisations.
The paper concludes that complete coverage, quasi-real time reporting, and household level detail of electricity consumption data in particular could support the delivery of population statistics and area-based social indicators, and we outline a research programme to address these opportunities.”