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Frontiers of Engineering Management

Front. Eng    2020, Vol. 7 Issue (2) : 287-300
Big Data to support sustainable urban energy planning: The EvoEnergy project
Moulay Larbi CHALAL(), Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY
School of Architecture, Design, and the Built Environment, Nottingham Trent University, Nottingham, UK
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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.

Keywords urban energy planning      sustainable planning      Big Data      household transition      energy prediction     
Corresponding Author(s): Moulay Larbi CHALAL   
Just Accepted Date: 27 December 2019   Online First Date: 27 February 2020    Issue Date: 27 May 2020
 Cite this article:   
Moulay Larbi CHALAL,Benachir MEDJDOUB,Nacer BEZAI, et al. Big Data to support sustainable urban energy planning: The EvoEnergy project[J]. Front. Eng, 2020, 7(2): 287-300.
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Fig.1  Architecture of EvoEnergy.
Fig.2  3D model of the Sneinton area in Nottingham in EvoEnergy.
Fig.3  Summary of a household energy history and socio-economic profile on mouse hover.
Fig.4  Household energy prediction module.
Fig.5  Comparison of two households’ transition patterns and energy consumption figures.
Fig.6  The methodology flowchart of this research in relation to our previous work (Medjdoub and Chalal, 2017).
Fig.7  Pie charts showing the distribution of age groups over the BHPS and UKHLS data sets.
Transition target Year of transition Model Goodness of fit (McFadden’s R2) obs ll (null) ll (model) df AIC LR chi2 (6) Prob.>chi2
Couples with children 1 Pooled model 0.626 480 -188.064 -70.40656 6 152.81 7.68 0.2625
Model with interaction effects 0.646 480 -188.064 -66.56636 12 157.13
2 Pooled model 0.514 269 -104.314 -23.03278 6 58.065 9.91 0.1284
Model with interaction effects 0.527 269 -104.314 -18.07673 12 60.15
Couples without children 1 Pooled model 0.361 1092 -413.4964 -264.3681 7 542.73 8.18 0.3167
Model with interaction effects 0.371 1092 -413.4964 -260.2763 14 548.55
2 Pooled model 0.468 693 -259.5675 -138.1586 7 290.31 10.18 0.1788
Model with interaction effects 0.487 693 -259.5675 -133.0703 14 294.14
Lone parents 1 Pooled model 0.683 4166 -1609.361 -510.5721 8 1037.14 6.12 0.6342
Model with interaction effects 0.685 4166 -1609.361 -507.5138 16 1047.02
2 Pooled model 0.514 2690 -1015.016 -260.9618 8 537.92 3.46 0.9021
Model with interaction effects 0.534 2690 -1015.016 -259.2309 16 550.46
Tab.1  The results of the Chow likelihood test comparing the coefficients of transition models resulting from BHPS and UKHLS
Mann–Whitney U test Kolmogorov–Smirnov Z test
Transition rates Transition rates
Mann–Whitney U 15.500 Most extreme differences Absolute 0.429
Wilcoxon W 43.500 Positive 0.429
Z -1.151 Negative 0.000
Asymp. sig. (2-tailed) 0.250 Kolmogorov–Smirnov Z 0.802
Exact sig. [2*(1-tailed sig.)] 0.259b Asymp. sig. (2-tailed) 0.541
Tab.2  The results of the Mann–Whitney U and Kolmogorov–Smirnov Z test statisticsa
Fig.8  The decrease in the percentage of single non-elderly households over different waves of BHPS and UKHLS as a result of them moving to other household types such as couple without children.
1 year
2 years
1 year
2 years
1 year
2 years
Log10 annual electricity usage BHPS 0.11** 0.12** 0.11** 0.093** 0.16** 0.13**
UKHLS 0.114** 0.117** 0.1157** 0.1043** 0.142** 0.152**
Square root of annual gas usage BHPS 0.008 0.005 0.114** 0.091** 0.160** 0.135**
UKHLS 0.01 0.009 0.129** 0.0835** 0.148** 0.115**
Tab.3  Comparison of impact of household transition on energy consumption across BHPS and UKHLS
Fig.9  The validation process of the developed energy prediction model.
Fig.10  Clustered bar graph representing the estimated and actual EPC energy figures of the chosen householders.
Fig.11  Discrepancies between the estimated energy figures using BHPS and the EPC energy data reported using the mean percentage error index (MPE).
Fig.12  Discrepancies between the estimated energy figures using UKHLS and the EPC energy data reported using the mean percentage error index (MPE).
Homogeneity of variance 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
  Summary of the homogeneity of variance analysis of different socio-economic and demographic variables across the BHPS and UKHLS data sets
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