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14 March 2024

Factors Influencing Electricity Consumption in Rural Households

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Facultad de Ingenieria, Universidad Distrital Francisco Jose de Caldas, Bogotá 110231, Colombia
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Energies2024, 17(6), 1392;https://doi.org/10.3390/en17061392
This article belongs to the Special Issue Rural Renewable Energy Utilization and Electrification II
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Abstract

Over time, several relationships have been defined between electricity consumption and a region’s social and economic variables, with income as the main factor. This paper uses multiple correspondence analysis to identify the categories of dwellings and, from a graphical point of view (positioning maps), the effects of the different characteristics that influence the electricity consumption of households in rural areas of Cundinamarca, Colombia. In this analysis, the consumption of residential users responded mainly to what they can afford or acquire based on their income, consumption habits, and the characteristics of the technology. Furthermore, this study highlights the implications of these findings for policymakers and energy providers, providing valuable insights for developing targeted strategies to promote energy efficiency and sustainability in rural areas. This research contributes to a deeper understanding of the dynamics of electricity consumption and highlights the importance of tailoring energy-related interventions to the specific socio-economic context of rural communities, in this case in Cundinamarca.

1. Introduction

In several countries, understanding the intricate dynamics of energy consumption in rural households is of paramount importance, revolving around three fundamental pillars: (a) energy for cooking, an indispensable necessity for all households; (b) energy for heating, critical for basic survival; and (c) electricity for powering various household activities, including study, work, food preparation, and entertainment [1,2,3]. The behavior of electricity in rural households presents some unique characteristics and challenges that differ from those in urban households [4]. Often, the availability and reliability of electricity in these areas can be limited, which influences how residents use electricity [5]. In addition, some rural households may have limited access to modern appliances due to reliance on traditional energy sources such as firewood or charcoal, and they use more rudimentary technologies instead [2,4].
In 2022, global electricity’s share in final energy consumption remained steady at 20.4%, a three-point increase since 2010. Notable growth in electrification was seen in Asia, especially in China, while the Middle East and Latin America also experienced increases. However, regions like North America, Europe, Australia, and Africa maintained relatively stable levels of electrification [6]. Globally, households consume a significant amount of electricity, with estimated figures suggesting that the residential sector accounts for around 20% of total electricity consumption [7,8], and a third of the final electricity in the EU is consumed by households [9]. In Colombia, the residential sector represents a significant portion, approximately 20% of the country’s total energy consumption [10]. Energy-intensive refrigeration, lighting, and cooking needs mainly contribute to that figure. Households predominantly rely on electricity and fuelwood to meet these needs, which account for about 31% and 28% of the total energy market, respectively [10]. While urban areas have increasingly been adopting energy-efficient appliances, rural areas rely on traditional energy sources such as firewood, charcoal, and rudimentary tools [10]. It is worth noting, however, that each region within a country has unique dynamics in energy use and availability of energy sources, making it essential to tailor energy policies and programs to specific regional contexts.
Most research on household energy consumption focuses on the energy ladder [4,11,12,13,14,15]; this theory indicates that households tend to move from more polluting and less efficient energy sources to cleaner and more efficient energy sources as their income increases and their access to energy technologies and services improves, with electricity at the top of the ladder. Therefore, the socio-economic status of each household plays a central role in shaping these energy consumption patterns, as higher incomes often lead to increased energy access and use [2,16,17,18,19,20,21]. However, a comprehensive understanding of rural household energy behavior requires a more holistic approach considering various social variables, including housing construction characteristics and the use of electrical and electronic appliances [1,2,16,17,18,19,22,23,24,25,26,27,28]. Furthermore, it is crucial to highlight that, in order to deal with this complexity, simple approaches that allow these variables to be effectively grouped are needed. In this sense, clear and concise graphs are one of the best tools to represent and understand the relationships between these variables. These graphs simplify the visualization of complex data and make it easy to identify significant patterns and trends in the energy behavior of rural households.
This paper presents an innovative methodology to analyze and identify the factors differentiating electricity consumption patterns among households in rural Colombia. To achieve this, data obtained from the Sustainable Rural Electrification Programs (PERS, in Spanish) were used (https://sig.upme.gov.co/SIPERS/TableuResources, accessed on 28 January 2024), advanced statistical techniques, such as multiple correspondence analysis, were applied, and visual mapping techniques to provide a graphical representation of the findings were employed. The novelty lies in the simplicity and effectiveness of using the positioning map to identify relationships between multiple variables with different categories. Understanding the factors that influence household energy use in rural areas allows us to understand the energy needs of these communities, which in some cases are economically lagging behind urban areas, facilitating the planning of electrical infrastructure and the more efficient allocation of resources. It also helps to identify areas of opportunity to promote more efficient practices and technologies in the use of electrical energy in the household. This may include awareness campaigns, incentive programs for the purchase of efficient appliances, and guidance on more economical and environmentally friendly consumption habits. This case study focused on Cundinamarca, which provides a diverse and representative sample of rural energy consumption in Colombia.
The structure of this paper is as follows: Section 2 discusses the multifaceted factors associated with household energy consumption in rural areas. It also provides a detailed description of the PERS, highlighting their objectives and methodology. Section 3 presents the multiple correspondence analysis methodology and criteria used for its implementation in this study. Analysis and discussion are included in later sections, where the main findings, in the context of rural energy consumption in Cundinamarca, are presented and interpreted. Finally, the conclusions summarize this research’s critical findings, implications, and avenues for further exploration in rural energy consumption dynamics and policy formulation.

3. Data and Methodology

Multiple correspondence analysis (MCA) allows the graphical representation (on a Cartesian plane) of the pattern of relationships between the categories of qualitative analyzed variables (ordinal or nominal), identifying the similarities and associations or the influence of the different variables. MCA helps to describe patterns of relationships distinctively using geometric methods by locating each variable/unit of analysis as a point in a low-dimensional space. MCA can be used to map both variables and individuals, allowing the construction of complex visual maps whose structure can be interpreted.
The heterogeneous set of analysis units must be represented on a plane where the dimensions have enough inertia to explain the variables. The first dimension explains as much variance as possible; the second dimension is orthogonal to the first and shows as much of the remaining variance as possible. The inertia is equal to the chi-square statistic (χ2) divided by the total, and it indicates how much of the variation in the original data is retained in the dimensional solution, which graphically represents the distance between the object category and its mean.
Depending on the typology of the individuals or the groups represented, the interpretation of the graphs will be through a perception of items being more or less close. Therefore, individuals with similar characteristics will appear close in space, and at the same time, each one of the characteristics will be in the space of the individuals.
In the present research, this relational analysis tool allowed us to establish correlations between the consumption of electricity and the variables of the PERS surveys, proving the existence of similar characteristics in the households studied.
Figure 4 illustrates the procedure developed to generate location maps. Based on the PERS database for Cundinamarca (1346 surveys), data preprocessing or filtering was performed using the variable of interest, electricity consumption, obtained from billing records (708 surveys). Surveys without consumption data were excluded. The monthly electricity consumption data were then categorized into three groups: Group 1: 1–90 kWh (low consumption), Group 2: 90–180 kWh (medium consumption), and Group 3: 180–300 kWh (high consumption). These categories were then cross-referenced with over 60 variables in various analyzed location maps. Values greater than 300 kWh were omitted as they were assumed to belong to population centers beyond the scope of this research. For some variables, it was deemed beneficial to group data into ranges to ensure more representative categories.
Figure 4. Flowchart of the multiple correspondence analysis (MCA) applied to the PERS Cundinamarca database.
Based on the 708 surveys analyzed (taking into account only the homes that had electricity consumption information from the PERS—Cundinamarca surveys), the electrical and electronic devices with the highest participation in households were the mobile phone (97.9%), refrigerator or freezer (85.6%), TV (85.1%), blender (73.2%), stereo or radio (52.9%), iron (36.0%), washing machine (29.3%), fan or air conditioner (6.5%), and computer (5.9%). The same order can be observed when grouped by level of consumption (see Figure 5).
Figure 5. Ownership of electrical appliances with regard to consumption. PERS—Cundinamarca (2016).

4. Results

Position maps analyze the relationships between factors or categories of qualitative variables. In Figure 6, Group 1 (1–90 kWh) can be observed on the left side, where users do not own any electrical appliances. Looking at Figure 5, this conclusion regarding the ownership of household appliances cannot be observed. However, this group does have TVs (*<0.5 TVs/person and between *1.5–2 TVs/person). Households with medium consumption (Group 2) have a refrigerator, a mixer, an iron, a washing machine, and a stereo or a radio. The number of TV sets per household ranges from 0.5 to 1. Group 3 has the same characteristics as Group 2, but they have more than two TVs per household. They have computers, air conditioners or fans.
Figure 6. Positioning map of main appliances, TV per person, and consumption.
Regarding the variable TV per person, Group 1 (low consumption) has a higher range than those with higher consumption (see Table 7). The same conclusion can be observed in Figure 6 (closeness of the values). Additionally, in Table 7, it can be observed that there is more than one TV per person in the household.
Table 7. TVs/people.
The low-consumption group has less than one light bulb per room, and the predominant technology is incandescent (Figure 7). This technology was withdrawn from the Colombian market in 2014; however, it is still being imported [51], and it is used in places above 1500 m above sea level (m.a.s.l.) to improve thermal comfort. The light bulb technology in the medium-consumption group is CFL (Compact Fluorescent Light Bulb), with at least one light bulb per room. The high-consumption group has the same characteristics as the medium-consumption group, but with more than one bulb per room. In addition, they have a fan or air conditioner when close to the variable < 500 m.a.s.l.
Figure 7. Positioning map of altitude, illumination, bulbs/room, and consumption.
Looking at the graphs independently, it is possible to conclude that the low-consumption group is found, at higher heights, with incandescent bulbs and less than one bulb per room (see Figure 8).
Figure 8. Altitude, illumination, bulbs/room, and consumption.
Figure 9 shows the relationships between television technology, hours of television viewing, and household income. In Group 1, CRT (Cathode-Ray Tube) technology predominates, with a daily use of up to 3 h and the lowest household income (up to USD 160 per month). In Group 2, the TV was used for from 3 to 8 h, CRT, LED, and LCD technologies predominate, and users had an average income (from USD 160 to USD 475 monthly). Group 3 is associated with a higher income (TV was used more than 8 h daily, including all TV technologies (CRT, LED, LCD, and plasma).
Figure 9. Positioning map of TV technology, hours of use (TV), income, and consumption.
When observing the graphs independently (see Figure 10 and Table 8), it is possible to reach the same conclusion: the most significant number of televisions operate with CRT technology. For the low-usage-duration categories (“<1” and “1–3” h), older technologies such as CRT have a relatively high share. This suggests that users may prefer older technologies for sporadic or short-term use. In the extended-use categories (“3–8” and “>8” h), LCD and LED technologies dominate, with significantly higher percentages compared to CRT and plasma. This suggests that newer technologies are preferred for longer viewing sessions.
Figure 10. TV technology, hours of use (TV), and consumption.
Table 8. TV technology, hours of service (TV), and consumption.
Table 9 shows a clear trend of decreasing consumption levels as income increases. The percentages of households with low consumption are higher in the lower-income row labels (“<32”, “32–48”), while high consumption is more prevalent in the higher-income row labels (“>953”). In addition, the row labels corresponding to lower incomes (“<32”, “32–48”) have significantly higher percentages of households with low consumption levels. This suggests that families with lower incomes tend to have lower consumption in relative terms.
Table 9. Income vs. consumption.
Similarly, as we move towards the middle-income row labels (“48–64”, “64–159”, “159–239”), there is a progressive increase in the percentages of households with average consumption. This suggests that middle-income families tend to have more balanced consumption levels. The row labels corresponding to higher incomes (“239–318”, “318–476”, “476–636”, “636–953”, “>953”) have significantly higher percentages of households with high consumption levels. This indicates that higher-income families tend to have relatively higher consumption levels. In the middle-income row labels (“48–64”, “64–159”, “159–239”), there is variability in the percentages of households with medium and high incomes. This suggests that there is diversity in consumption patterns at these income levels.
The behavior of the cost of electricity is not obvious because there are other loads, such as public lighting and subsidies, included in the bill that distort this value, so the relationship between bills and income was analyzed. We found that the higher values (20–40% and >40%) are related to Group 3. In contrast, the other values correspond to Groups 1 and 2 (see Figure 11).
Figure 11. Positioning map of numbers of pieces of equipment, households’ size, % invoice/income, and consumption.
Group 1 households are families with few persons (1–3), and Group 2 is related to households with 4–6 persons and 7–9 persons. Regarding the number of electrical appliances, the smallest number of appliances (1–3) is found in Group 1 (see Figure 11).
The conclusions shown in Figure 12 are similar to those of Figure 11. According to the surveys, it is common to have between four and eight electric devices. The proportion of households with a high number of devices (>12) is considerably lower than the proportion with a moderate number of devices (4–8 and 9–12). This suggests that most households tend to have a moderate number of appliances. Households with smaller sizes (1–3 persons) have a significantly higher representation in all consumption categories. A high percentage of households spend more than 5% on electricity. This suggests that smaller households face a greater economic burden from energy expenditures.
Figure 12. Number of appliances, households’ size, % invoice/income, and consumption.
The relationship between the socio-economic level and energy consumption should be more obvious, as shown in Figure 13. A smaller number of rooms (less than three and between four and six) is related to Group 1, as well as handmade construction materials in the walls (bamboo, rush mat, other vegetables, mud, adobe, clay, rough wood plank) and floors (land, sand, cement, gravel, rough wood, board, plank).
Figure 13. Positioning map of socio-economic level, numbers of rooms, floor and wall materials, and consumption.
Households in Groups 2 and 3 have more rooms; the walls are made of brick, block, stone, polished wood, and prefabricated materials; the floors are made of tiles and bricks (see Figure 13).
The attributes of Group 2, cement floors and brick walls, are located near the center of the map. In an MCA, the closer to the center, the less different it will be, so these attributes are not good consumption differentiators.
In Figure 14, households with 4–6 rooms have a significantly higher proportion in all consumption categories, suggesting that the house size does not influence Cundinamarca’s case. Most of the houses have cement floors and brick walls; they do not show consumption patterns according to the material. As in the previous conclusions, the most representative social background population is Group 2.
Figure 14. Socio-economic level, number of rooms, floor and wall materials, and consumption.
Figure 15 shows the occupations of the family members; those in Group 1 are related to households, agriculture, livestock, and forestry. Group 2 consists of students, businesspeople, and pensioners. Services, mining, industry, and manufacturing activities are related to Group 3.
Figure 15. Positioning map of economic activity, education level, and consumption.
Figure 15 shows the higher education level of the family members, but no relationship can be established because the values of χ2 are close to 0.
Table 10 summarizes the results of the multiple correspondence analysis and compares the main characteristics of the three consumption groups.
Table 10. Comparison of the characteristics of the three groups identified.

5. Discussion

Although several papers have analyzed the factors influencing electricity consumption at the household level, there are few cases of rural households. MCA is a technique for analyzing categorical variables that is useful when it is required to get a general understanding of how these variables are related, compare subgroups, and understand trends. An issue lies in the fact that the resulting maps can be more user-friendly if they contain fewer than five variables, as demonstrated in this instance.
The results found in this study are similar to those reported in the literature, but MCA was not mentioned.
The higher the household income, the higher the electricity consumption (Figure 8 and Figure 9, and Table 10) [16,17,18,19]. This is due to more appliances (Figure 10 and Figure 11) and the number of people in the household (Figure 10 and Figure 11). In addition, income is directly related to bill payment (Figure 10 and Figure 11), rooms (Figure 12 and Figure 13), wall and floor materials (Figure 12 and Figure 13), occupancy (Figure 14), and the use of more efficient technology (Figure 8 and Figure 9, Table 7 and Table 8) [16,17,18,19,22,23,24,43].
Generally, the higher the socio-economic level, the higher the electricity consumption in households, but in this case, this is not true.
The number of appliances and their use are influenced by household size (Figure 4 and Figure 5) and lifestyle (hours of use), which correlate with electricity consumption. The purchase of these appliances is related to income (Figure 5 and Figure 9), expenses (Figure 10 and Figure 11), and appliance characteristics (Figure 7 and Figure 9) [16,17,18,19].
The “Jevons paradox” indicates that demand can increase when a technological process increases in efficiency. In this case, there is an “environmental rebound effect” in which the introduction of more energy-efficient technologies can increase total energy consumption because instantaneous consumption decreases, but the time of use increases (Figure 7 and Table 8) [52].
The presence of household appliances does not imply electricity consumption since user consumption habits (routines) of the users and the frequency of use of the appliances must be taken into account (Figure 10).
The occupation of family members influences the time spent at home and, therefore, the use of electrical appliances, which in turn influences consumption (Figure 14).

6. Conclusions

Many studies have argued that income is one of the fundamental factors in determining household electricity consumption. However, there are other decisive factors for household electricity consumption related to income that have been studied in this paper, such as the number of appliances, educational level, and house characteristics; the literature in this area has focused on developing countries, but rural areas had not been covered yet. We address this research gap and extend the existing literature on sustainable energy development using data collected in Cundinamarca, a mainly rural area in Colombia.
The authors used a descriptive approach to analyze the collected data, and MCA was also used. Multiple correspondence analysis is a technique for analyzing categorical variables, a form of factor analysis for categorical data. It is best used when it is necessary to get a general understanding of the way in which categorical variables are related. The resulting maps are difficult to use with more than five or six variables.
In the case of Cundinamarca, the factors that influence household electricity consumption are income, household size, rooms, appliances, technology, and hours of use.
In the rural households of Cundinamarca, the most common appliances are the refrigerator and the television. However, the emerging concern about the proper use of new technologies that facilitate the social integration of communities, such as telephones, PCs, etc. is worth mentioning.
The multiple correspondence analysis identified similar characteristics of rural households in Cundinamarca, which were divided into three groups: low consumption (low income, few people in the house, few appliances, and old technology); medium consumption (average income and increased number of people, entertainment use, food preservation, and personal care devices); the high-consumption group is like the medium-consumption group, but with higher income, more appliances, and more hours of use.

Author Contributions

D.S.G.-M.: writing, original draft preparation, investigation, review, editing, conceptualization, methodology, software, and formal analysis. F.S.: analysis, writing, review, and editing. C.L.T.: analysis, writing, review, and editing. H.E.R.-C.: analysis, writing, review, and editing. W.A.R.: analysis, writing, review, and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors thank the research group GISE3, which is part of the project “Methodology for decision-making of electrification projects in isolated rural areas, from a systemic and sustainable development approach” registered in the CIDC of the Universidad Distrital Francisco Jose de Caldas.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

CFLCompact Fluorescent Light Bulb
CRTCathode-Ray Tube
DANEDepartamento administrativo nacional de estadística (National Administrative Department of Statistics)
HHHouseholds
IPSEInstituto de Planificación y Promoción de Soluciones Energéticas para Zonas No Interconectadas (Institute for Planning and Promotion of Energy Solutions for Non-Interconnected Zones)
m.a.s.l.Meters above sea level
MCAMultiple correspondence analysis
PERSPrograma de Electrificacion Rural Sostenible (Sustainable Rural Electrification Programs)
PSUPrimary sampling unit
SDGSustainable Development Goals
SDG7Sustainable Development Goal 7 (Ensure universal access to affordable, reliable, sustainable, and modern energy)
TV/pplTVs/people
UPMEUnidad de Planeación Minero Energética (Mining and Energy Planning Unit)

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