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Assessing Households’ Gas and Electricity Consumption: A Case Study of Djelfa, Algeria

Quaestiones Geographicae
Volume 37 (2018): Issue 4 (December 2018)
By:
Open Access
|Dec 2018

Figures & Tables

Fig. 1

Climate zone in the department of Djelfa. Based on DTR 3.2: 2004.
Climate zone in the department of Djelfa. Based on DTR 3.2: 2004.

Fig. 2

Temperature variation between 1930 and 2004.
Temperature variation between 1930 and 2004.

Fig. 3

Consumption of (a) electricity and (b) gas by residential buildings in 2013.
Consumption of (a) electricity and (b) gas by residential buildings in 2013.

Fig. 4

Gas and electricity consumption in 2008 per municipality.
Gas and electricity consumption in 2008 per municipality.

Fig. 5

(a) housing number and size, (b) household size, room occupancy and average number of rooms per housing, (c) appliance ownership (TV, cooker, washing machine, refrigerator and air conditioner).
(a) housing number and size, (b) household size, room occupancy and average number of rooms per housing, (c) appliance ownership (TV, cooker, washing machine, refrigerator and air conditioner).

Fig. 6

PCA of all the variables used to cluster municipalities according to their population characteristics, dwelling typology, population distribution, climate zone, and appliance ownership.Legend: A_Z = arid zone, SA_Z= semi-arid zone H_S = household size, R_O = room occupancy, M_A = mean area, HQ_M = population number living in headquarter municipality, S_Z = population number living in scattered zone, S_A = population living in secondary agglomeration, DT = density, CD = collective dwelling, IVD = individual dwelling, TD = traditional dwelling, G_C = gas consumption per municipality, E_C = electricity consumption per municipality, G_H = gas consumption per housing, E_H = electricity consumption per housing, E_L = education level, App = appliance ownership, AA = average age per housing per municipality.
PCA of all the variables used to cluster municipalities according to their population characteristics, dwelling typology, population distribution, climate zone, and appliance ownership.Legend: A_Z = arid zone, SA_Z= semi-arid zone H_S = household size, R_O = room occupancy, M_A = mean area, HQ_M = population number living in headquarter municipality, S_Z = population number living in scattered zone, S_A = population living in secondary agglomeration, DT = density, CD = collective dwelling, IVD = individual dwelling, TD = traditional dwelling, G_C = gas consumption per municipality, E_C = electricity consumption per municipality, G_H = gas consumption per housing, E_H = electricity consumption per housing, E_L = education level, App = appliance ownership, AA = average age per housing per municipality.

Fig. 7

Projection of municipalities according to PCA.
Projection of municipalities according to PCA.

Fig. 8

Path analysis containing all possible hypotheses.
Path analysis containing all possible hypotheses.

Fig. 9

Standardised direct effect for gas consumption per kwh/person.m2.y.
Standardised direct effect for gas consumption per kwh/person.m2.y.

Fig. 10

Direct and indirect effect of variables on household gas consumption. With: H_S: household size, E_L: education level, R_O: room occupancy, HRP>60: household with a responsible person over the age of 60, DT: density, H_A: housing area.
Direct and indirect effect of variables on household gas consumption. With: H_S: household size, E_L: education level, R_O: room occupancy, HRP>60: household with a responsible person over the age of 60, DT: density, H_A: housing area.

Fig. 11

Direct impact of variables on per-capita electricity consumption.
Direct impact of variables on per-capita electricity consumption.

Fig. 12

Direct and indirect effect of variables on household electricity consumption. With: H_S: household size, E_L: education level, R_O: room occupancy, HRP>60: household with a responsible person over the age of 60, DT: density, App: appliance ownership, H_A: housing area.
Direct and indirect effect of variables on household electricity consumption. With: H_S: household size, E_L: education level, R_O: room occupancy, HRP>60: household with a responsible person over the age of 60, DT: density, App: appliance ownership, H_A: housing area.

Fig. 13

Direct and indirect effect of variables on gas and electricity household consumption. With: H_S: household size, E_L: education level, R_O: room occupancy, HRP>60: household with a responsible person over the age of 60, DT: density, App: appliance ownership, H_A: housing area.
Direct and indirect effect of variables on gas and electricity household consumption. With: H_S: household size, E_L: education level, R_O: room occupancy, HRP>60: household with a responsible person over the age of 60, DT: density, App: appliance ownership, H_A: housing area.

Direct, indirect and total effect of household, housing and density variables on per-capita gas consumption_

HRP>60Room OccupancyHouseholds sizeEducation LevelHousing areaLog 10 Density
effectDirIndtotDirIndtotDirIndtotDirIndtotDirIndtotDirIndtot
M_A.000.000.000−.745.000−.7451.047.0001.047.000.000.000.000.000.000.000.000.000
density.805.000.805.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000
gas*.000.207.207.000.174.174−.493−.245−.738.239.000.239−.234.000−.234.257.000.257

Electricity Model fit Indices_

dfχ2Probability levelRMSEANFICFI
Electricity model116.3520.8490.0570.9801.000

Direct, indirect and total impact of variables on per-capita electricity consumption_ Effect Household size Education level Room occupancy HRP>60 Log 10 density Appliance Housing area

EffectHousehold sizeEducation levelRoom occupancyHRP>60Log 10 densityApplianceHousing area
DirindirtotDirindirtotDirindirtotDirindirtotDirindirtotDirindirtotDirindirtot
density.000.000.000.000.000.000.000.000.000.805.000.805.000.000.000.000.000.000.000.000.000
App.000.000.0001.070.0001.070−.163.000−.163−.480.222−.259.275.000.275.000.000.000.000.000.000
M_A1.047.0001.047.000.000.000−.745.000−.745.000.000.000.000.000.000.000.000.000.000.000.000
Elec*.000−.868−.868.000.207.207−.395.586.192.000−.396−.396−.430.053−.376.194.000.194−.829.000−.829

Path analysis: standardised direct effect_

EstimateStandard ErrP
housing area←households size1.047***
housing area←room occupancy−.745***
log 10 density←HRP>60.805***
gas_kwhm2_p←log 10 density.257.021**
gas_kwhm2_p←education level.239.032**
gas_kwhm2_p←housing area−.234.052*
gas_kwhm2_p←households size−.493***

Bivariate correlations between energy consumption and households, housing variables, density and appliance ownership_

A-AELLog10HRP>60M_AH-SR-OLog10DtLog10 (A-C)CTVRefrWMApp
EUICorrelation.594**.521**.514**−.593**−.781**−.321.335*.280.348*.490**.358*.253.431**
Sig..000.001.001.000.000.057.046.099.038.002.032.137.009
GCorrelation.590**.551**.564**−.543**−.747**−.330*.395*.287.390*−−−−
Sig..000.000.000.001.000.049.017.089.019−−−−
ECorrelation.362*.018−.166−.782**−.746**−.089−.356*.086−.078.044−.234.006
Sig..030.916.332.000.000.604.033.616−.653.800.170.973

Model fit Indices of per-capita gas consumption_

dfχ2Probability levelRMSEANFICFI
gas model88.9080.350.0570.9640.996

Summary of the gas and electricity model_

Electricity model summary (R2=0.937)Gas model summary (R2= 0.986)
modelunstandardised coefficientsstandardised coefficientsSig.modelunstandardised coefficientsstandardised coefficientsSig.
BStd. errorBetaBStd. errorBeta
(constant)0.9270.2680.002(constant)1.635.163.000
log10_electricity subscribers1.2390.0971.1780.000log10_gas_subscribers1.141.0501.097.000
log10_number_of housings−0.3720.145−0.2380.015log10_population number−.161.065−.120.020

Mean values, standard deviation and test of normality of the variables_

With log10 transformation
Variables (2013)MeanStd deviationSkewnessKurtosisSkewnessKurtosis
gas subscribers3126.867523.274.3521.320.800.13
gas consumption (Kth)80545.56210208.394.8425.860.720.11
electricity subscribers3968.758209.254.1319.380.740.56
electricity consumption (mwh)10139.7521255.034.1819.660.510.56
number of population35667.1464194.614.5122.920.321.77
number of housing6256.299888.584.0318.070.910.59

Characteristics of clusters_

Cluster1234
Label▪▾•♦
Housing energy consumption++−−
Municipality energy consumption+−+−
Size3 (8.3 %)26 (72.2%)1 (2.8 %)6 (16.7 %)
Variablesclimate zonesemi-aridsemi-aridsemi-aridarid
population distributiondensityaverage densitylow densitydenselow density
municipality centremoderately populatedthinly populatedhighly populatedthinly populated
secondary agglomerationmoderately populatedthinly populatedthinly populatedthinly populated
scattered zonemoderately populatedmoderately populatedmoderately populatedhighly populated
population characteristicsaverage agehighest ratemoderate ratelowest ratelowest rate
education levelhighest ratelow ratemoderate ratelow rate
dwelling occupancymean areamoderatehighmoderateless
room occupancymoderatemoderatehighest ratelowest rate
household sizelow ratemoderate ratelowest ratehighest rate
dwelling typologycollective dwellinghigh ratelow ratehighest ratelowest rate
individual dwellinghigh ratelow ratehighest ratelow rate
traditional dwellinghigh ratemoderate ratehigh ratemoderate
appliance ownershiphighest ratemoderate ratemoderate ratelowest rate

Normality tests of the models_

Validation test
Electricity modelGas model
SkewnessKurtosisKolmogorov-Smirnova (KS)Shapiro-WilkSkewnessKurtosisKolmogorov-SmirnovaShapiro-Wilk
.745.426.200*.092*−.334−.718.200*.570*

Path analysis: significance of direct impact_

EstimateP
log 10 density←HRP>60.805***
appliance←log density.275.008**
appliance←room occupancy−.163.014**
appliance←education local1.070***
appliance←HRP>60−.480***
housing area←room occupancy−.745***
housing area←household size1.047***
E_kwhm2_p←housing area−.829***
E_kwhm2_p←log density−.430***
E_kwhm2_p←appliance.194.071*
E_kwhm2_p←room occupancy−.395***
DOI: https://doi.org/10.2478/quageo-2018-0034 | Journal eISSN: 2081-6383 | Journal ISSN: 2082-2103
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Language: English
Page range: 111 - 129
Submitted on: Mar 25, 2018
Published on: Dec 31, 2018
Published by: Adam Mickiewicz University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Algeria,
gas consumption,
electricity consumption,
household characteristics,
housing characteristics,
density,
statistical approach,
GIS
Related subjects:
Geosciences,
Geography

© 2018 Soufiane Boukarta, Ewa Berezowska-Azzag, published by Adam Mickiewicz University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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