Electricity consumption and household characteristics: Implications for census-taking in a smart metered future

Abstract 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.

[1]  Andrew J. Wright What is the relationship between built form and energy use in dwellings , 2008 .

[2]  Antonio Lima,et al.  Personalized routing for multitudes in smart cities , 2015, EPJ Data Science.

[3]  Silvia Santini,et al.  Automatic socio-economic classification of households using electricity consumption data , 2013, e-Energy '13.

[4]  Kevin J. Lomas,et al.  Identifying trends in the use of domestic appliances from household electricity consumption measurements , 2008 .

[5]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

[6]  Silvia Santini,et al.  Towards automatic classification of private households using electricity consumption data , 2012, BuildSys@SenSys.

[7]  Barry Leventhal,et al.  Beyond the 2011 Census in the United Kingdom: With an International Perspective , 2011 .

[8]  John R. Williams,et al.  Clustering Household Electricity Use Profiles , 2013, MLSDA '13.

[9]  Aidan Duffy,et al.  Evaluation of time series techniques to characterise domestic electricity demand , 2013 .

[10]  Keith Dugmore Information Collected by Commercial Companies: What Might be of Value to Official Statistics? The Case of the UK Office for National Statistics , 2010 .

[11]  Patrick James,et al.  The role of digital trace data in supporting the collection of population statistics – the case for smart metered electricity consumption data , 2016 .

[12]  Furong Li,et al.  Demand response in the UK's domestic sector , 2009 .

[13]  Sean Lyons,et al.  Reducing household electricity demand through smart metering: The role of improved information about energy saving , 2014 .

[14]  J. Gareth Polhill,et al.  The North East Scotland Energy Monitoring Project: Exploring relationships between household occupants and energy usage , 2014, Energy and Buildings.

[15]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[16]  Harriet Bulkeley,et al.  Peak electricity demand and the flexibility of everyday life , 2014 .

[17]  Brian Norton,et al.  Real-life energy use in the UK: How occupancy and dwelling characteristics affect domestic electricity use , 2008 .

[18]  Peter Grindrod,et al.  A new error measure for forecasts of household-level, high resolution electrical energy consumption , 2014 .

[19]  Fintan McLoughlin,et al.  Characterising Domestic Electricity Demand for Customer Load Profile Segmentation , 2013 .

[20]  Murali Haran,et al.  Piecing together the past: statistical insights into paleoclimatic reconstructions , 2010 .

[21]  Philip Price,et al.  Methods for Analyzing Electric Load Shape and its Variability , 2010 .

[22]  Dongjin Song,et al.  High resolution population estimates from telecommunications data , 2015, EPJ Data Science.

[23]  Ian Richardson,et al.  Smart meter data: Balancing consumer privacy concerns with legitimate applications , 2012 .

[24]  David J. Martin,et al.  Last of the censuses? The future of small area population data , 2006 .

[25]  A. Druckman,et al.  Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model , 2008 .

[26]  Mark Graham,et al.  Geography and the future of big data, big data and the future of geography , 2013 .

[27]  A. Wright,et al.  The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations , 2007 .

[28]  Piet Daas,et al.  Official statistics and Big Data , 2014 .

[29]  J. Reades,et al.  On the value of Digital Traces for commercial strategy and public policy: Telecommunications data as a case study , 2012 .

[30]  Roberto Napoli,et al.  Electric energy customer characterisation for developing dedicated market strategies , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[31]  Anna Kovacs-Györi Mapping Urban Practices Through Mobile Phone Data , 2017 .

[32]  Malcolm K. Hughes,et al.  The future of the past—an earth system framework for high resolution paleoclimatology: editorial essay , 2009 .

[33]  Michael Conlon,et al.  Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study , 2012 .