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

Soufiane Boukarta; Ewa Berezowska-Azzag

Open Access

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

Soufiane Boukarta

and

Ewa Berezowska-Azzag

| Dec 31, 2018

Quaestiones Geographicae

Volume 37 (2018): Issue 4 (December 2018)

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Page range: 111 - 129

Received: Mar 25, 2018

DOI: https://doi.org/10.2478/quageo-2018-0034

Keywords
Algeria, gas consumption, electricity consumption, household characteristics, housing characteristics, density, statistical approach, GIS

© 2018 Soufiane Boukarta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Temperature variation between 1930 and 2004.

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

Gas and electricity consumption in 2008 per municipality.

(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).

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.

Projection of municipalities according to PCA.

Path analysis containing all possible hypotheses.

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

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 impact of variables on per-capita electricity consumption.

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 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>60			Room Occupancy			Households size			Education Level			Housing area			Log 10 Density
effect	Dir	Ind	tot	Dir	Ind	tot	Dir	Ind	tot	Dir	Ind	tot	Dir	Ind	tot	Dir	Ind	tot
M_A	.000	.000	.000	−.745	.000	−.745	1.047	.000	1.047	.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.

	df	χ2	Probability level	RMSEA	NFI	CFI
Electricity model	11	6.352	0.849	0.057	0.980	1.000

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

Effect	Household size			Education level			Room occupancy			HRP>60			Log 10 density			Appliance			Housing area
Effect	Dir	indir	tot	Dir	indir	tot	Dir	indir	tot	Dir	indir	tot	Dir	indir	tot	Dir	indir	tot	Dir	indir	tot
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	.000	1.070	.000	1.070	−.163	.000	−.163	−.480	.222	−.259	.275	.000	.275	.000	.000	.000	.000	.000	.000
M_A	1.047	.000	1.047	.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.

			Estimate	P
housing area	←	households size	1.047	***
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-A	EL	Log10HRP>60	M_A	H-S	R-O	Log10Dt	Log10 (A-C)	C	TV	Refr	WM	App
EUI	Correlation	.594^**	.521^**	.514^**	−.593^**	−.781^**	−.321	.335^*	.280	.348^*	.490^**	.358^*	.253	.431^**
EUI	Sig.	.000	.001	.001	.000	.000	.057	.046	.099	.038	.002	.032	.137	.009
G	Correlation	.590^**	.551^**	.564^**	−.543^**	−.747^**	−.330^*	.395^*	.287	.390^*	−	−	−	−
G	Sig.	.000	.000	.000	.001	.000	.049	.017	.089	.019	−	−	−	−
E	Correlation	.362^*	.018	−.166	−.782^**	−.746^**	−.089	−.356^*	.086	−	.078	.044	−.234	.006
E	Sig.	.030	.916	.332	.000	.000	.604	.033	.616	−	.653	.800	.170	.973

Model fit Indices of per-capita gas consumption.

	df	χ2	Probability level	RMSEA	NFI	CFI
gas model	8	8.908	0.35	0.057	0.964	0.996

Summary of the gas and electricity model.

Electricity model summary (R²=0.937)					Gas model summary (R²= 0.986)
model	unstandardised coefficients		standardised coefficients	Sig.	model	unstandardised coefficients		standardised coefficients	Sig.
model	B	Std. error	Beta	Sig.	model	B	Std. error	Beta	Sig.
(constant)	0.927	0.268		0.002	(constant)	1.635	.163		.000
log10_electricity subscribers	1.239	0.097	1.178	0.000	log10_gas_subscribers	1.141	.050	1.097	.000
log10_number_of housings	−0.372	0.145	−0.238	0.015	log10_population number	−.161	.065	−.120	.020

Mean values, standard deviation and test of normality of the variables.

					With log10 transformation
Variables (2013)	Mean	Std deviation	Skewness	Kurtosis	Skewness	Kurtosis
gas subscribers	3126.86	7523.27	4.35	21.32	0.80	0.13
gas consumption (Kth)	80545.56	210208.39	4.84	25.86	0.72	0.11
electricity subscribers	3968.75	8209.25	4.13	19.38	0.74	0.56
electricity consumption (mwh)	10139.75	21255.03	4.18	19.66	0.51	0.56
number of population	35667.14	64194.61	4.51	22.92	0.32	1.77
number of housing	6256.29	9888.58	4.03	18.07	0.91	0.59

Characteristics of clusters.

Cluster			1	2	3	4
Label			▪	▾	•	♦
Housing energy consumption			+	+	−	−
Municipality energy consumption			+	−	+	−
Size			3 (8.3 %)	26 (72.2%)	1 (2.8 %)	6 (16.7 %)
Variables	climate zone		semi-arid	semi-arid	semi-arid	arid
	population distribution	density	average density	low density	dense	low density
		municipality centre	moderately populated	thinly populated	highly populated	thinly populated
		secondary agglomeration	moderately populated	thinly populated	thinly populated	thinly populated
		scattered zone	moderately populated	moderately populated	moderately populated	highly populated
	population characteristics	average age	highest rate	moderate rate	lowest rate	lowest rate
	population characteristics	education level	highest rate	low rate	moderate rate	low rate
	dwelling occupancy	mean area	moderate	high	moderate	less
		room occupancy	moderate	moderate	highest rate	lowest rate
		household size	low rate	moderate rate	lowest rate	highest rate
	dwelling typology	collective dwelling	high rate	low rate	highest rate	lowest rate
		individual dwelling	high rate	low rate	highest rate	low rate
		traditional dwelling	high rate	moderate rate	high rate	moderate
	appliance ownership		highest rate	moderate rate	moderate rate	lowest rate

Normality tests of the models.

Validation test
Electricity model				Gas model
Skewness	Kurtosis	Kolmogorov-Smirnova (KS)	Shapiro-Wilk	Skewness	Kurtosis	Kolmogorov-Smirnova	Shapiro-Wilk
.745	.426	.200*	.092*	−.334	−.718	.200^*	.570*

Path analysis: significance of direct impact.

			Estimate	P
log 10 density	←	HRP>60	.805	***
appliance	←	log density	.275	.008**
appliance	←	room occupancy	−.163	.014**
appliance	←	education local	1.070	***
appliance	←	HRP>60	−.480	***
housing area	←	room occupancy	−.745	***
housing area	←	household size	1.047	***
E_kwhm2_p	←	housing area	−.829	***
E_kwhm2_p	←	log density	−.430	***
E_kwhm2_p	←	appliance	.194	.071*
E_kwhm2_p	←	room occupancy	−.395	***

eISSN:: 2081-6383
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Assessing Households’ Gas and Electricity Consumption: A Case Study of Djelfa, Algeria

Published Online: Dec 31, 2018

Page range: 111 - 129

Received: Mar 25, 2018

DOI: https://doi.org/10.2478/quageo-2018-0034

Keywords
Algeria, gas consumption, electricity consumption, household characteristics, housing characteristics, density, statistical approach, GIS

© 2018 Soufiane Boukarta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Electricity Model fit Indices.

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

Path analysis: standardised direct effect.

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

Model fit Indices of per-capita gas consumption.

Summary of the gas and electricity model.

Mean values, standard deviation and test of normality of the variables.

Characteristics of clusters.

Normality tests of the models.

Path analysis: significance of direct impact.

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

Published Online: Dec 31, 2018

Page range: 111 - 129

Received: Mar 25, 2018

DOI: https://doi.org/10.2478/quageo-2018-0034

KeywordsAlgeria, gas consumption, electricity consumption, household characteristics, housing characteristics, density, statistical approach, GIS

© 2018 Soufiane Boukarta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

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

Electricity Model fit Indices.

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

Path analysis: standardised direct effect.

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

Model fit Indices of per-capita gas consumption.

Summary of the gas and electricity model.

Mean values, standard deviation and test of normality of the variables.

Characteristics of clusters.

Normality tests of the models.

Path analysis: significance of direct impact.

Keywords
Algeria, gas consumption, electricity consumption, household characteristics, housing characteristics, density, statistical approach, GIS