Big Data to support sustainable urban energy planning: The EvoEnergy project

Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socio-economic to meet the strict CO 2 emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey, namely, the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE 5%) when compared to actual energy performance certificate data. From this premise, we recommend researchers, who are working on data-driven energy consumption forecasting, to consider merging the BHPS and UKHLS data sets. This will, in turn, enable them to capture the bigger picture of different energy phenomena such as fuel poverty; consequently, anticipate problems with policy prior to their occurrence. Finally, the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.

[1]  Sanem Sergici,et al.  The Impact of Informational Feedback on Energy Consumption -- A Survey of the Experimental Evidence , 2009 .

[2]  S. Bamberg,et al.  Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour , 2007 .

[3]  C. Vlek,et al.  Measurement and Determinants of Environmentally Significant Consumer Behavior , 2002 .

[4]  J. Quigley,et al.  Residential energy use and conservation: Economics and demographics , 2012 .

[5]  C. Vlek,et al.  Encouraging pro-environmental behaviour : An integrative review and research agenda , 2009 .

[6]  S. Barr,et al.  The household energy gap: examining the divide between habitual- and purchase-related conservation behaviours , 2005 .

[7]  G. Chow.  Tests of equality between sets of coefficients in two linear regressions (econometrics voi 28 , 1960 .

[8]  Shanlin Yang,et al.  Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies , 2018 .

[9]  Ardalan Khosrowpour,et al.  A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation , 2018 .

[10]  Simonetta Longhi,et al.  Residential energy expenditures and the relevance of changes in household circumstances , 2015 .

[11]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[12]  M. Chalal,et al.  Impact of household transitions on domestic energy consumption and its applicability to urban energy planning , 2017 .

[13]  Merve Bedir,et al.  Determinants of electricity consumption in Dutch dwellings , 2013 .

[14]  P. Tiwari.  Architectural, Demographic, and Economic Causes of Electricity Consumption in Bombay , 2000 .

[15]  Zaid Chalabi,et al.  Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes , 2016 .

[16]  W. Kamakura,et al.  Household Life Cycles and Lifestyles in the United States , 2006 .

[17]  W. Abrahamse,et al.  How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings? , 2009 .

[18]  J. Fuinhas,et al.  Are renewables affecting income distribution and increasing the risk of household poverty? , 2019, Energy.

[19]  Evie McCrum-Gardner,et al.  Which is the correct statistical test to use? , 2008, The British journal of oral & maxillofacial surgery.

[20]  Dingtao Zhao,et al.  Who exhibits more energy-saving behavior in direct and indirect ways in china? The role of psychological factors and socio-demographics , 2016 .

[21]  Karen Stenner,et al.  The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review , 2015 .

[22]  M. McGoldrick,et al.  The family life cycle. , 1980 .

[23]  Ada Ferrer-i-Carbonell,et al.  The ex post impact of an energy tax on household energy demand , 2004 .

[24]  W. Poortinga,et al.  Values, Environmental Concern, and Environmental Behavior , 2004 .

[25]  C. Vlassoff,et al.  Social and Economic Research , 1956, Nature.

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

[27]  Keith A. Jones,et al.  Domestic energy consumption—What role do comfort, habit, and knowledge about the heating system play? , 2013 .

[28]  K. Blok,et al.  Household energy requirement and value patterns , 2007 .

[29]  Stata Press,et al.  Stata Longitudinal-Data/Panel-Data Reference Manual: Release 11 , 2009 .

[30]  Rory V. Jones,et al.  The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings , 2015 .

[31]  Industrial Strategy,et al.  Energy Consumption in the UK , 2020 .

[32]  Kevin J. Lomas,et al.  Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics , 2015 .

[33]  Stephanie L. McFall,et al.  Understanding Society: early findings from the first wave of the UK's household longitudinal study , 2011 .

[34]  Leif Gustavsson,et al.  Factors influencing energy efficiency investments in existing Swedish residential buildings , 2010 .

[35]  Michael White,et al.  Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review , 2016 .