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 CO2 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.
. [J]. Frontiers of Engineering Management, 2020, 7(2): 287-300.
Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY. Big Data to support sustainable urban energy planning: The EvoEnergy project. Front. Eng, 2020, 7(2): 287-300.
If partially heterogeneous, what category is homogenous/heterogeneous?
Gender
Homogeneous
N/A
Age
Mostly heterogeneous
Aged 36–45 is homogeneous
Marital status
Mostly homogeneous
Married is heterogeneous
Level of education
Mostly heterogeneous
A-Level is homogeneous
Tenure mode
Mostly heterogeneous
Rented from employer and private landlords are homogeneous
Dwelling type
Mostly homogenous
Living in terraced dwelling is heterogeneous
Dwelling size
Mostly heterogeneous
3-bedroom dwelling is homogeneous
Socio-economic class
Mostly heterogeneous
Professional occupations and unskilled workers are homogeneous
1
W Abrahamse, L Steg (2009). How do socio-demographic and psychological factors relate to households’ direct and indirect energy use and savings? Journal of Economic Psychology, 30(5): 711–720 https://doi.org/10.1016/j.joep.2009.05.006
2
ADECOE (2016). Is UK’s approach to fuel poverty suffering from a poverty of ideas? Available at: adecoe.co.uk/2016/03/10
3
S Bamberg, G Möser (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27(1): 14–25 https://doi.org/10.1016/j.jenvp.2006.12.002
4
S Barr, A W Gilg, N Ford (2005). The household energy gap: Examining the divide between habitual- and purchase-related conservation behaviours. Energy Policy, 33(11): 1425–1444 https://doi.org/10.1016/j.enpol.2003.12.016
P H G Berkhout, A Ferrer-i-Carbonell, J C Muskens (2004). The ex post impact of an energy tax on household energy demand. Energy Economics, 26(3): 297–317 https://doi.org/10.1016/j.eneco.2004.04.002
7
BRE (2013). Report 3: Metered fuel consumption—Including annex on high energy users. In: Energy Follow Up Survey, 2011. London: Department of Energy and Climate Change
8
D Brounen, N Kok, J Quigley (2012). Residential energy use and conservation: Economics and demographics. European Economic Review, 56(5): 931–945 https://doi.org/10.1016/j.euroecorev.2012.02.007
9
M Chalal (2018). A Smart Urban Energy Prediction System to Support Energy Planning in the Residential Sector. Dissertation for the Doctoral Degree. Nottingham: Nottingham Trent University
10
M Chalal, M Benachir, M White, R Shrahily (2016). Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review. Renewable & Sustainable Energy Reviews, 64: 761–776 https://doi.org/10.1016/j.rser.2016.06.040
11
G C Chow (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3): 591–605 https://doi.org/10.2307/1910133
12
Department for Business, Energy & Industrial Strategy (BEIS) (2017a). Energy consumption in the UK. 2017 ed. London: Crown
13
Department for Business, Energy & Industrial Strategy (BEIS) (2017b). Household energy efficiency headline release. 2017 ed. London: Crown
14
A Druckman, T Jackson (2008). Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model. Energy Policy, 36(8): 3177–3192 https://doi.org/10.1016/j.enpol.2008.03.021
15
R Y Du, W A Kamakura (2006). Household life cycles and lifestyles in the United States. Journal of Marketing Research, 43(1): 121–132 https://doi.org/10.1509/jmkr.43.1.121
16
J Edwards, A Townsend (2011) CIOB carbon action 2050: Buildings under refurbishment and retrofit. Bracknell, UK: The Chartered Institute of Building (CIOB)
17
A Faruqui, S Sergici, A Sharif (2010). The impact of informational feedback on energy consumption—A survey of the experimental evidence. Energy, 35(4): 1598–1608 https://doi.org/10.1016/j.energy.2009.07.042
18
E R Frederiks, K Stenner, E V Hobman (2015). The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review. Energies, 8(1): 573–609 https://doi.org/10.3390/en8010573
19
B Gatersleben, L Steg, C Vlek (2002). Measurement and determinants of environmentally significant consumer behavior. Environment and Behavior, 34(3): 335–362 https://doi.org/10.1177/0013916502034003004
20
W H Greene (2002). Econometric Analysis. Upper Saddle River, New Jersey: Prentice Hall
21
Z F Guo, K L Zhou, C Zhang, X H Lu, W Chen, S L Yang (2018). Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies. Renewable & Sustainable Energy Reviews, 81(1): 399–412 https://doi.org/10.1016/j.rser.2017.07.046
22
B Hitesh (2018). Family life-cycle. Marketing Management Articles
23
G Huebner, D Shipworth, I Hamilton, Z Chalabi, T Oreszczyn (2016). Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes. Applied Energy, 177: 692–702 https://doi.org/10.1016/j.apenergy.2016.04.075
24
G M Huebner, J Cooper, K Jones (2013). Domestic energy consumption—What role do comfort, habit, and knowledge about the heating system play? Energy and Building, 66: 626–636 https://doi.org/10.1016/j.enbuild.2013.07.043
25
Institute for Social and Economic Research (2016). British Household Panel Survey (BHPS)
26
R V Jones, A Fuertes, K J Lomas (2015). The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings. Renewable & Sustainable Energy Reviews, 43: 901–917 https://doi.org/10.1016/j.rser.2014.11.084
27
R V Jones, K J Lomas (2015). Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics. Energy and Building, 101: 24–34 https://doi.org/10.1016/j.enbuild.2015.04.052
28
A Kavousian, R Rajagopal, M Fischer (2013). Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior. Energy, 55: 184–194 https://doi.org/10.1016/j.energy.2013.03.086
29
A Khosrowpour, R K Jain, J E Taylor, G Peschiera, J Chen, R Gulbinas (2018). A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation. Applied Energy, 218: 304–316 https://doi.org/10.1016/j.apenergy.2018.02.148
30
S Longhi (2015). Residential energy expenditures and the relevance of changes in household circumstances. Energy Economics, 49: 440–450 https://doi.org/10.1016/j.eneco.2015.03.018
S L McFall, C Garrington (2011). Understanding society: Early findings from the first wave of the UK’s Household Longitudinal Study. Colchester, Essex: Institute for Social and Economic Research, University of Essex
33
B Medjdoub, M L Chalal (2017). Impact of household transitions on domestic energy consumption and its applicability to urban energy planning. Frontiers of Engineering Management, 4(2): 171–183 https://doi.org/10.15302/J-FEM-2017029
34
G Nair, L Gustavsson, K Mahapatra (2010). Factors influencing energy efficiency investments in existing Swedish residential buildings. Energy Policy, 38(6): 2956–2963 https://doi.org/10.1016/j.enpol.2010.01.033
35
Office for National Statistics (2018). Population estimates for UK, England and Wales, Scotland and Northern Ireland: Mid-2016
36
D S Pereira, A C Marques, J A Fuinhas (2019). Are renewables affecting income distribution and increasing the risk of household poverty? Energy, 170: 791–803 https://doi.org/10.1016/j.energy.2018.12.199
37
W Poortinga, L Steg, C Vlek (2004). Values, environmental concern, and environmental behavior: A study into household energy use. Environment and Behavior, 36(1): 70–93 https://doi.org/10.1177/0013916503251466
38
Stata (2015). Longitudinal-Data/Panel-Data Reference Manual, Release 14. College Station, TX: Stata Press
39
L Steg, C Vlek (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3): 309–317 https://doi.org/10.1016/j.jenvp.2008.10.004
40
P Tiwari (2000). Architectural, demographic, and economic causes of electricity consumption in Bombay. Journal of Policy Modeling, 22(1): 81–98 https://doi.org/10.1016/S0161-8938(98)00003-9
41
Understanding Society (2017). Understanding Society—The UK Household Longitudinal Study. The Institute for Social and Economic Research, University of Essex
S Yang, Y Zhang, D Zhao (2016). Who exhibits more energy-saving behavior in direct and indirect ways in China? The role of psychological factors and socio-demographics. Energy Policy, 93: 196–205 https://doi.org/10.1016/j.enpol.2016.02.018
