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Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression

Department of Urban Planning and Engineering, Hanyang University, Seoul 04763, Korea
Author to whom correspondence should be addressed.
Buildings 2022, 12(5), 678;
Received: 26 April 2022 / Revised: 15 May 2022 / Accepted: 17 May 2022 / Published: 19 May 2022
(This article belongs to the Collection Strategies for Sustainable Urban Development)


The recent increase in energy consumption worldwide has accelerated global warming. Thus, developed countries are aiming to reduce energy consumption in cities and promote eco-friendly policies. Buildings account for most of the energy used in a city. Therefore, it is necessary to identify the factors that affect electrical energy consumption in urban buildings. In this study, we use multiscale geographically weighted regression (MGWR) to analyze these urban characteristic factors at the global and local scales in Seoul, Korea. It is found that population and household characteristics, outdoor temperature, green and water areas, building area according to building usage, and construction age significantly affect the electrical energy consumption of buildings. In addition, the influences of these variables change with the region. Variables with different coefficients by region are winter temperature, green and water area, and households with three or more persons. The results confirm that even within a city, the influence of the aforementioned factors varies in terms of spatial distribution and patterns. This study is significant as it carried out basic research for energy consumption reduction in buildings by deriving related influencing factors.

1. Introduction

Recently, global warming owing to an unprecedented increase in energy consumption has emerged as a growing concern. According to predictions made using the Shared Socioeconomic Pathways (SSP) scenario linked to socioeconomic activities in terms of population, economy, land use, energy use, and carbon emissions, the global average temperature will increase by 1.5 °C by 2040 compared to preindustrial levels, regardless of the scenario [1]. Climate change is the reality we are faced with. Therefore, efforts have been undertaken by various organizations and governments to develop carbon-neutral cities. Given increasing urban population and the development of high-rise buildings, buildings now use 2.5 times more electricity than they did in the past [2]. In addition, global building energy consumption accounts for 40% of the total global energy consumption. Owing to continuous urbanization, high-density, high-rise buildings are the primary residential constructions in cities [3]. In light of this, many studies have been conducted to elucidate the causes and consequences of global warming. The literature suggests that anthropogenic activities have accelerated warming of the atmosphere, ocean, and land [4]. To cope with climate change, the European Union (EU) has initiated various environmental policies and proposed to reduce energy consumption and carbon emissions from human activities by 40% by 2030 [5]. Similarly, various efforts have been undertaken to reduce the energy consumption of buildings in cities for mitigating the environmental toll of urbanization [6]. In this context, various physical factors that affect the energy consumption of buildings have been studied [7,8,9]. Furthermore, to reduce the energy consumption of cities, it is crucial to identify the primary causes and phenomena affecting the energy consumption of buildings [10,11].
Several researchers have studied the electrical energy consumption of buildings [12,13,14,15,16,17]. In addition, numerous studies have reported that the electrical energy consumption of buildings has a spatial correlation [18,19,20,21]. Similarly, in this study, the urban characteristics of buildings in Seoul, Korea, and their correlation with the energy consumption of these buildings were explored. Buildings in cities are spatially distributed, and buildings with similar level characteristics appear in a dense form [22]. According to the first law of geography, each building exhibits spatial autocorrelation owing to this distribution [23]. Therefore, the energy consumption of these buildings, too, must exhibit spatial autocorrelation owing to the spatial pattern of buildings. Considering the aforementioned factors, in this study, the distribution of spatial patterns and the local relationships of each factor are analyzed using a geo-weighted model, which is a known spatial analysis technique.
Seoul is a large city with a population of 10 million. It is expected that the characteristics of each region within Seoul vary because the city has a large urban and green areas, and the Han River runs through the city from east to west. As such, Seoul has peculiar spatial patterns of energy use, but the existing studies reflecting the urban characteristics of Seoul are insufficient. Although many researchers have analyzed the energy consumption of Seoul, they have not considered the influence of spatial patterns [24,25,26,27,28]. In addition, these researchers considered only partial variables in the analysis. By contrast, variables mentioned as significant or important are considered comprehensively in the present study. Among the previous studies on Seoul’s energy consumption, the geographically weighted regression (GWR) model was used to analyze energy consumption in only one study [29]. However, even in this study, carbon dioxide emissions were used to measure energy consumption, which is an indirect method. In light of these points, we conduct a more developed study based on spatial analysis and variables that considers the characteristics of Seoul.
The objective of this study is to identify the variables that affect the electrical energy consumption of buildings by performing spatial regression analysis. Moreover, we attempt to determine the effects of significant variables on electrical energy consumption in urban spaces. GWR is a representative model for performing spatial analysis. It can be used to examine the influence of each regression formula considering the bandwidth corresponding to the analysis unit. Thus, the GWR model can provide results that are relevant to the objective of this study. Herein, the effect of diverse variables on electrical energy usage is examined comprehensively by using the multiscale geographically weighted regression (MGWR) model, which can check the local part among models that consider spatial influence. The MGWR model is based on the GWR model, and it sets the bandwidth differently for each independent variable. The characteristic that the spatial distribution of each variable is different is reflected in the analysis. Therefore, the MGWR model facilitates more accurate local unit analysis. Consequently, this analysis method that considers space is expected to yield significant results in this study. Finally, the analysis results of this study can be used as basic building energy consumption data at the local level to achieve the energy reduction goal set by the city in the future.
The remainder of this paper is organized as follows. Section 2 discusses the previous studies related to the use of electrical energy and spatial characteristics of buildings. Section 3 describes the methodology used in the present study. The dependent and independent variables constructed to provide spatial and temporal explanations and a description of the analysis target are provided. In addition, the analysis methods used herein, namely GWR and MGWR, are discussed. Section 4 describes the basic statistical results, as well as the results of ordinary least squares (OLS) and MGWR analyses. Section 5 discusses the policy implications of this study based on the results presented in Section 4. Section 6 describes the significance and limitations of this study (Figure 1).

2. Literature Review

In this section, previous studies on the electrical energy consumption of buildings are discussed. Several studies have analyzed the electrical energy consumption of buildings based on their spatial characteristics and derived urban-characteristic factors that affect electrical energy usage in buildings. The characteristic factors considered include physical, socioeconomic, and environmental factors. Among them, studies on physical factors have made the most significant contributions to the literature. In these studies, it has been reported that various physical factors, such as the area, density, height, age of construction, and building materials, affect the electrical energy consumption of buildings [7,8,9,10,11,12,13,14]. In addition, more electrical energy is used for heating and cooling in high-density and high-rise buildings owing to solar radiation and restricted airflow around such buildings [30,31,32]. Furthermore, as the total floor area of buildings increases, the amount of electrical energy required increases. Several studies [33,34] have reported the use of glass windows and insulation materials as critical factors that influence the power consumption of buildings. It has been observed that older buildings have a weaker insulation effect. Therefore, they require more energy for heating and cooling.
Income, real estate, tax, and the number of household members are the socioeconomic factors that significantly influence energy consumption. In general, it is observed that the more affluent a region, the higher is the consumption of electrical energy. Similarly, income significantly influences energy consumption [35,36]. Studies on the effects of socioeconomic factors on energy consumption have analyzed the energy consumption of buildings based on their purpose [35,36,37]. It has been reported that the amount of energy consumed by a building varies depending on the primary use of the building. Therefore, the characteristics and electrical energy consumption patterns of residential, commercial, and industrial buildings differ [12,13,14,17]. The electricity consumption of a household is correlated to taxes, real estate prices [38], number of household members, and income [35]. In commercial buildings, factors such as population, income, building type, and amount of heating and cooling influence the amount of energy consumed [39]. The amount of electricity consumed based on building type can be ordered as follows: retail > office > restaurant > school. While the aforementioned order can differ somewhat depending on building characteristics in each country, retail and business buildings consume the highest amounts of energy.
Environmental factors, such as indoor and outdoor temperature, climate, wind speed, humidity, and vegetation, have various effects on energy consumption. Among the environmental factors, temperature has the most significant effect on electrical energy consumption [17,31,40,41,42,43,44,45]. In general, in a city with hot weather, owing to the strong heat island effect, electricity consumption is related to cooling. Similarly, in a city with cold weather, electricity consumption is related to heating. In general, owing to the heat island phenomenon, more energy is used for cooling in summer, but less energy is consumed for heating in winters [40,41]. Therefore, it can be concluded that temperature outside a building plays a significant role in determining the amount of energy required to maintain the internal temperature of the building. Many studies [35,36,37,38,39,40,41,42,43,44,45,46,47] have reported that vegetation is one of the factors that reduces the heat island effect in cities and, therefore, energy consumption, by lowering the ambient temperature. Therefore, green spaces, such as urban parks, vegetation zones, and water systems, need to be promoted in urban environments.
Our review of previous studies confirmed that various city-specific factors influence the electrical energy consumption of buildings. Recent studies have suggested that to reduce the energy consumption of buildings, it is necessary to check the energy consumption patterns of multiple buildings [7,13]. Given that these energy consumption patterns change according to the characteristics of cities, various other factors must be considered as well [18,35,36,37]. Characteristics such as demographic factors indicate that humans consume energy when engaging in social activities, which seem to follow a certain spatial pattern [35]. However, the spatial correlations of and changes in all variables are not equal. The unique spatial patterns of each variable affect energy consumption in various manners. Therefore, it is crucial to identify unique spatial patterns for identifying the factors affecting energy consumption [18]. Although several studies have considered spatial autocorrelation in the use of electrical energy, studies that comprehensively address urban characteristic factors are lacking. In light of this, this study uses a more advanced model than the GWR model and comprehensively accounts for the variables employed in the literature.
In sum up, our literature review confirmed the existence of various factors affecting the energy consumption of buildings, such as demographic factors, socioeconomic factors, environmental factors, and building characteristics. However, previous studies have not considered the spatial autocorrelation that may occur when analyzing these factors individually or in complex analyses. Therefore, in this study, the aforementioned factors are analyzed comprehensively.

3. Methodology

3.1. Electric Power Consumption Data and Variables

In this study, the GWR model was used to identify the urban characteristic factors that affected the electrical energy consumption of buildings in 424 administrative districts in Seoul in 2020. The electrical energy consumption data of these buildings in Seoul were used as the dependent variables. The values obtained by taking the natural logarithm of the total amount of electrical energy used in the buildings in each administrative dong were used as the dependent variables. The electrical energy consumption data of the buildings in question was provided by the Korea Real Estate Agency. The independent variables were composed based on the variables considered as the factors influencing the electrical energy consumption in buildings in previous studies (Figure 2).
First, a set of variables related to temperature, a factor that significantly affects the energy consumption of buildings, was constructed. The average temperatures during spring, summer, autumn, and winter were constructed as variables because the temperature changes across these four seasons are distinct in Korea. In addition, because the temperature in the city is affected by vegetation, the total area covered by vegetation and water bodies was constructed as a variable. Next, as the population and household variables, the living population and the numbers of single-person households, two-person households, and three or-more-person households were selected as variables. Considering that electricity consumption is generally high in regions with large active populations, living population data that aggregate the active population in a region were used instead of resident population data. Smartphone communication data from an area were used to derive the living population data. According to previous studies, the energy consumption per person increases as the number of household members decreases. In this study, the variables were set by categorizing the households into one-, two-, and three-or-more-person households based on the number of household members. Household income, which has been considered a significant factor affecting electricity consumption in previous studies [35,36], was considered in this study as well. In previous studies, the physical characteristics of buildings and building types were considered significant factors affecting electrical energy consumption. Therefore, in this study, the average number of floors; average age of buildings in administrative dongs (the smallest administrative district in Korea); and total floor areas of apartments, detached houses, commercial facilities, education facilities, and business facilities were considered as building type variables. Given that most of the industrial facilities in Seoul are light industries (Table 1), industrial facilities were not considered as a separate variable.

3.2. Spatial Analysis

3.2.1. GWR

General regression analysis methods do not consider spatial influence relationships and they describe only global relationships among variables [48]. GWR applies spatial weights to the existing OLS model to facilitate local spatial analysis. Hence, spatial autocorrelation and heterogeneity were considered through spatial weights. Spatial weights were applied to the GWR model by using the coordinates (u, v) of each spatial analysis unit [49] according to the following Equation (1).
y i = j = 0 m β j ( u i , v i ) x i j + ε i
In the above equation, y i represents the electrical energy consumption of an administrative dong i , and β j is the coefficient of the j independent variables of administrative dong i . The coordinates of each administrative dong u i , v i were applied to each space, as expressed in (1). A regression equation was derived for as many administrative dongs as possible, and the statistical analysis results of each target site were checked. In this study, the geographic weight of the GWR model was applied to the kernel function. Subsequently, a fixed kernel randomly selected by the authors and a statistically manipulated adaptive kernel were used. Because GWR yields local regression results, the spatial correlations and heterogeneity that conventional OLS cannot consider were accounted for. However, the GWR model is vulnerable to multicollinearity [50]. Various GWR models have been developed to mitigate this problem [51].

3.2.2. Multiscale GWR

The general GWR model is limited, in that it applies only one spatial scale. It considers only a single fixed bandwidth value in the analysis model. Thus, the model is vulnerable to multicollinearity. Additionally, each independent variable has global or local characteristics depending on the characteristics of the overall data. Therefore, considering the characteristics of the variables and the limitations of the GWR model, GWR analysis methods that apply a bandwidth suitable for each variable have been developed. Among such methods, mixed GWR separates global and local independent variables, and the multiscale GWR (MGWR) sets the local coefficient value that has the optimal Akaike Information Criterion (AICc) value in the analysis model [52].
Thus, the best model is selected based on the AICc value. In this study, the MGWR model was applied using a backfitting algorithm (Figure 3). In general, the initial local coefficient of the MGWR model is based on the results of GWR analysis, and the subsequent local coefficients are estimated through corrections. The local coefficients are estimated repeatedly until they reach the desired levels, and the same method is repeated for all independent variables. SOC-RSS (Residual Sum of Squares) (2) are used as the iterative work termination criteria. The coefficient values are estimated using the proportionality of the residual sum of squares (RSS) and the SOC-f method (3), which corrects the GWR analysis results. Therefore, in this study, we used the SOC-f method. When the SOC-f value was less than 0.0005, the next independent variable was set. The adaptive kernel function was applied to each independent variable to select the bandwidth value. In addition, the model in which AICc attained the minimum value was selected by combining the bandwidth values [53].
S O C R S S = | R S S n e w R S S o l d | R S S n e w
S O C f = j = 1 p i = 1 n ( f i j n e w f i j o l d ) 2 n i = 1 n ( j = 1 p f i j n e w ) 2
where SOC-RSS is the proportional change in the residual sum of squares, and SOC-f is the change in the GWR smoother.

4. Analysis Results

4.1. Descriptive Statistics

The reliability of the data constructed for analysis is confirmed through basic statistical analysis. In addition, the basic statistics serve as a starting point for conducting spatial regression analysis by displaying data distributions and patterns in this study. The descriptive statistics of the independent variables used in the analysis and the electrical energy consumption of buildings by administrative dong, the dependent variable in this study, are as follows: first, the number of administrative dongs in Seoul, which is the analysis target, is 424; the administrative dong is the unit of analysis in this study. Because the existing distribution of electrical energy consumption does not exhibit a general linear form, it is converted to a linear form by taking its natural logarithm. Among seasonal temperatures, the temperatures in spring and autumn are similar, as can be observed from the overall statistical values. By contrast, the summer and winter temperatures are noticeably different. The temperature distribution used in this study reflects the seasonal characteristics of Korea, which has four distinct seasons. Based on the results reported in previous studies, we considered temperature a significant factor affecting the amount of electrical energy required for heating and cooling in buildings. The average number of floors in each administrative dong varied from 2 to 18. The average building age in each dong ranged from 5 to 55 years. Among the variables pertaining to each building type, the areas of detached houses and education and business facilities are zero in several administrative dongs because these dongs house densely populated buildings of the same type. Household income levels ranged from 1836 USD per month to 5714 USD per month, and the average household income was 2849 USD per month (Table 2).

4.2. Evaluation of Model

According to the literature [18,19,20,21], there exists a spatial effect on the consumption of electrical energy in buildings. Therefore, a GWR analysis considering spatial autocorrelation was conducted. Before spatial regression analysis, the dependent variable, that is, the electrical energy consumption of each administrative dong, was examined to identify spatial autocorrelations. Moran’s index was calculated by performing Moran’s I analysis of ArcGIS to examine global spatial autocorrelations and spatial distribution patterns. A Moran’s index value of 0.1153, which represents a significant level, was obtained in the analysis. This indicated a positive spatial autocorrelation. Furthermore, the Z-Score value indicated that the degree of clustering was 6.249, meaning that the spatial pattern was extremely clustered (Table 3). Moran’s Index was calculated using the MGWR residual to determine whether the spatial autocorrelation was controlled by the MGWR model. The value of Moran’s Index was −0.0278, which was lower than the Moran’s Index value obtained using the OLS model. In addition, the probability for spatial autocorrelation of the MGWR model was not significant, meaning that spatial autocorrelation was controlled.
Subsequently, the fits of three models were compared, namely, the global OLS, spatial GWR, and MGWR models, considering various criteria. In this study, the residual sum of squares (RSS), AIC, AICc, R2, and adjusted R2 were considered to identify the most suitable model. The RSS, AIC, and AICc values decreased gradually in the order of OLS, GWR, and MGWR. This indicates that MGWR had the best model fit. In addition, the R2 of the OLS model was 0.555. However, the R2 of the GWR model was 0.661, R2 of the MGWR model was 0.74, and adjusted R2 of the MGWR model was 0.685. Thus, it can be concluded that the MGWR model had the highest explanatory power. Consequently, iterative work was performed to determine the optimal model fit and bandwidth of the MGWR model. Through iteration, the model with the lowest AICc was selected as the final model. In this study, 36 iterations were performed to determine the final model (Table 4).
In general GWR analysis, the adjusted R2 value indicates the explanatory power of the entire global model, and the local R2 value representing each variable locally is presented separately. In this study, the adjusted R2 value of the MGWR analysis model was 0.685. The region with the lowest local R2 value of 0.63 was located toward the northeast and southeast of Seoul. The region with the highest explanatory power was the southwestern region. Note that the closer it is to the region, the higher is its explanatory power. The region with the strongest explanatory power had an R2 value of 0.78, indicating the existence of regions with explanatory powers higher than 0.685, which was the overall explanatory power of the model (Figure 4).

4.3. Result of Spatial Analysis

In this study, the bandwidth value applied to each independent variable in the MGWR model differed from that in the GWR model. According to the spatial distribution of the data of each variable, iterative work was performed using an appropriate bandwidth value. As mentioned above, a total of 36 operations were performed. Variables representing 423 of the bandwidth values that appeared had the characteristics of global variables rather than local variables. Other variables included variables with strong local characteristics, such as summer temperature, winter temperature, households with three or more people, green and water-covered areas, average building age, average apartment area, average educational facility area, and average business facility area (Table 5).
The analysis results obtained using the global OLS model and the MGWR model were compared and reviewed. Firstly, the analysis results were reviewed centering on the variables significantly derived from OLS. The summer temperature exhibited a significant influence at 0.111, and the winter temperature exhibited a significant influence at −0.106. In the MGWR analysis, the average values of the local coefficients of these two variables had a positive influence. However, in terms of the minimum, median, and maximum values of winter temperature, there were regions with local negative and positive effects. In the OLS analysis results, living population had a significant positive influence, and in the MGWR results, it also had a positive influence globally. This finding is consistent with the results of previous studies, which indicated that the larger the population in an area, the greater the electrical energy consumption. Next, green and water areas had a significant positive influence. In general, green and water areas play a significant role in alleviating the urban heat island phenomenon. Furthermore, they have a negative influence on electrical energy consumption for cooling. However, note that green and water areas alone do not reduce the heat island phenomenon. The heat island phenomenon is additionally affected by the permeability, density, and surrounding environment of an area [42]. However, the OLS results obtained in this study showed their positive effect, which is contrary to the results obtained in a previous study. This can be attributed to the factors other than green and water areas that significantly reduced electrical energy consumption.
In terms of building area, significant positive results were observed for apartments, education buildings, and business buildings. According to the OLS and MGWR analysis results, the building areas of apartments and business buildings had a global positive effect. Regarding the area of educational buildings, the OLS result was positive, but the MGWR results found regions with a negative influence. In addition, both the OLS and MGWR analyses showed the significant positive influence of household income (Table 6).
The local results of the variables that appear significantly in the MGWR analysis results are as follows. Summer temperature had significant positive effects in most of the regions. These results were similar to those of the related studies in which MGWR was used, and the higher the summer temperature, the stronger the influence on energy consumption [54]. Electrical energy consumption due to cooling is high in summer. Therefore, it was identified to have a positive effect across the city [45]. The bandwidth used for summer temperature was 235. Therefore, summer temperature was shown to be a variable that influenced a large area of the city. This indicates that the summer temperature across administrative dongs in Seoul was similarly high overall. In addition, it was observed that the closer a region was to Gangnam, the more significant the effect of summer temperature on energy consumption in that region. Notably, this result confirmed that the influence of summer temperature was weaker in regions closer to Mt. Bukhan, which is in the northern part of Seoul. This reflects the spatial characteristics of a region with small building areas and large mountainous areas (Figure 5a). Winter temperature also had significant positive effects. However, it had a negative influence on the CBD areas, Gangnam, and Jongro. The bandwidth used for winter temperature was 78. Thus, winter temperature had a smaller bandwidth than summer temperature, indicating a regional temperature difference, and we can conclude that the energy used for heating increases as the temperature outside buildings in a region decreases (Figure 5b). These results suggest a relationship between seasonal temperature and energy usage, and it was confirmed that energy usage differs depending on the region owing to the temperature differences stemming from the heat island effect or density [31].
Previous studies have reported that green and water areas alleviate the urban heat island phenomenon and reduce electrical energy consumption by maintaining the temperature in the city at a comfortable level [42]. However, in contrast to the results of previous studies, the analysis results of this study confirmed that the average coefficient value had a positive influence on electric energy consumption. A related study using MGWR confirmed that all variables related to green areas had positive effects, unlike the analysis results of this study [54]. By contrast, we assumed that green areas may have local effects depending on the region and that there may be differences in influence depending on the area, as mentioned in the Introduction. In addition, by checking the local coefficient of green and water areas, it can be inferred that the green and water areas had a weak negative influence on Seoul overall. However, strong positive effects were observed in the Gangnam and Gangseo areas (Figure 5c). The bandwidth of the green and water area variable was 109. Thus, the effect of green and water areas in Seoul is more local than city-wide. Therefore, it can be concluded that a strong positive influence was observed for a high average local coefficient value. This result can be attributed to the high building density and high electrical energy consumption, despite the large-scale green and water areas. The Gimpo Airport (8.63 km2) in Gangseo consumes the highest amount of electrical energy among all of the administrative dongs in Seoul. In the Jamsil district, which corresponds to the Songpa area, there are large green areas, such as Seokchon Lake and Olympic Park. In addition, many high-rise buildings, such as the Jamsil Lotte Tower and Lotte World, are located in this area. However, Eunpyeong and Gwanak, the regions in which Mt. Bukhan and Mt. Gwanak are located, respectively, and Yeongdeungpo and Yongsan, which are close to Mt. Gwanak and the Han River, were found to be negatively affected. We assumed that the green and water areas in these regions reduced the use of electrical energy by maintaining the temperature at a relatively comfortable level. These results show that superficial green and water areas alone cannot significantly affect energy reduction, as mentioned in previous studies (Figure 6).
In the case of household income, significant positive effects were confirmed in all regions except Gangseo. A study conducted using GWR yielded the same results as the present study, with a positive impact across the city [55]. This result is consistent with the social belief that high-income people use more electrical energy than low-income people. This was confirmed by the fact that a greater amount of electrical energy was consumed in Gangdong and Gangnam in 2020, where the average household income is relatively high, compared to that in Gangseo, where the average household income is relatively low. In a related study conducted using MGWR, energy consumption increased as income increased across the city. Moreover, as the interregional income difference increased, the influence increased, as reported in a previous study [56] (Figure 5d).
Living population had no significant influence on the Gangbuk region around Mt. Bukhan. However, the closer a region was to Gangnam and Gangseo, the more significant the positive influence. This is consistent with the results of previous studies, in which it was demonstrated that the electrical energy consumption of buildings increases in areas with a high density of buildings and more floating populations than that in mountainous terrain [3,4]. In another study conducted using MGWR, the effect of population differed depending on the region, but it was found to have a positive effect overall. This is consistent with the general conclusion that the higher the population density, the higher is the energy consumption [57]. In addition, the analysis results indicated that the impact of living population on Seoul is large (Figure 5e). In addition, various local factors affect households with three or more persons in Seoul. The bandwidth used for households with three or more persons was 103. In other studies conducted using MGWR, the influence of regional differences was different from that in the present study. Previous studies have mentioned households as an important variable. Moreover, the previous studies reported consistent results throughout the city with no regional differences [54,58]. Unlike previous studies, we identified differences in influence due to the regional characteristics mentioned in the introduction. Living population had a positive influence in the Gangnam, Yeouido, Mapo, and Yongsan regions. Thus, it can be inferred that in these areas, the higher the number of households with three or more persons, the higher the electricity consumption. This was validated by the fact that these regions have households with relatively high income levels in Seoul. Therefore, they possibly use a large amount of electrical energy. By contrast, a negative influence was observed in Gangseo, Jungnang, Gwanak, Gangdong, and the entire city, meaning that the higher the number of households with three or more people, the lower the electrical energy consumption. These results indicate that as the number of household members increases, the amount of energy consumed by one person per unit area in a building decreases. Moreover, previous studies have demonstrated that an increase in the number of household members leads to a decrease in energy consumption because it increases the population density [38]. Thus, it can be concluded that in general, an increase in the number of household members leads to a reduction in energy consumption (Figure 5f).
Among building types, apartment buildings had a significant positive influence in the Gangdong and Gangbuk regions, but no significant effect in the Gangseo region. In the case of apartment buildings, given that multiple households reside in one building, the amount of electrical energy used by the building is higher than that used in other types of housing. This claim was validated by the results of the present study (Figure 7a). By contrast, detached houses had a negative influence. Unlike apartments, a detached house is often inhabited by a single household. Hence, compared to an entire apartment building, the amount of energy consumed is lower because the total floor area of a detached house is lower than that of an apartment building (Figure 7b). Educational buildings had a significant positive influence, except in the Gangnam, Eunpyeong, and Mapo regions. The bandwidth used for educational buildings was 145. In the Gwanak region, where Seoul National University is located, educational buildings had the strongest influence. This indicates that universities consume a large amount of electrical energy (Figure 7c). Among educational buildings, the amount of energy used by each school level was different, but university buildings used large amounts of energy [59]. Furthermore, it was found that building age had a significant positive effect. OLS analysis did not yield any significance, but MGWR analysis yielded some significance in the city center, which houses relatively old buildings. Thus, as reported in a previous study [60], the results of this study confirmed that building age affects the insulation and energy consumption of buildings. Therefore, old buildings should be repaired to reduce electrical energy consumption and increase energy efficiency (Figure 7d).

5. Discussions

In this study, we conducted an MGWR analysis to identify the factors affecting electrical energy consumption, especially the factors that were not detected in OLS analysis. The analysis results confirmed that the influence of the factors considered here may differ across regions. Therefore, we propose several suggestions for reducing the electrical energy consumption of buildings based on the analysis results.
We found that the outdoor temperature during winter and summer has a significant effect on electrical energy consumption. In summer, electricity consumption increases throughout the city because of an increase in cooling demand. This is consistent with the results of previous studies [10,38,54]. By contrast, several regions consumed more electrical energy during winter to maintain a relatively comfortable temperature in the building. This result confirmed the existence of regional differences in electrical energy consumption used for heating in winter, which did not appear in other studies. The area with the highest electrical energy consumption had a high density of buildings. It was found that this area used plenty of electrical energy for heating and cooling to maintain comfortable temperatures inside buildings. Thus, the application of technology that maintains indoor temperatures can reduce the electrical energy required for heating and cooling in winter and summer, respectively.
Furthermore, the OLS analysis results indicated that the green and water areas have a positive influence. However, the MGWR results show that the degree of influence differs across regions. This indicates that green and water areas do not necessarily affect the electrical energy consumption within a city. However, in some regions, green and water areas can somewhat influence electrical energy consumption. Even in the areas with plenty of green areas and water bodies, we found areas with high temperatures depending on solar orientation. Likewise, even in areas with a high density of green areas, some patches may not be significantly influenced by green cover. Therefore, a novel method is required for assessing the relationship between energy consumption and the density and degree of influence of green and water areas.
In the case of the influence of population, the analysis results showed that the higher the absolute population, the higher the energy consumption; this result is consistent with the existing perception that a compact city is more suitable for reducing energy consumption.
Studies have reported that older buildings consume more electrical energy, which is consistent with our analysis results. Therefore, the insulation effect of old buildings should be improved through repair and reconstruction. The Jongro region of Seoul has many aging buildings, and their energy efficiency is low. Hence, active redevelopment and repair work is required in Seoul to increase the electrical energy efficiency of these buildings.

6. Conclusions

In this study, characteristic urban factors affecting the energy consumption of buildings were analyzed across the administrative districts in Seoul in 2020. The local influences of variables were determined using the MGWR model, a local spatial regression model. This model was used to consider the spatial autocorrelations of the electrical energy consumption of buildings by applying a backfitting algorithm to the GWR model. Consequently, the resulting model confirmed that the influences of the factors affecting electrical energy usage varies by region.
Moreover, the analysis results confirmed that summer and winter temperatures affect the electrical energy consumption of buildings. In addition, they confirmed that in some regions, green and water areas reduce electrical energy consumption by alleviating the heat island phenomenon, as reported in previous studies. However, in regions with large-scale facilities or buildings, the influence of large green areas or water areas was not significant. Hence, the relationship between green and water areas and electrical energy consumption warrants further investigation. In the case of population and households, the analysis results showed that the number of household members had a significant effect on the use of electrical energy. An increase in the number of household members reduced electrical energy consumption. In addition, electricity consumption varied according to building type. Furthermore, it was confirmed that the influence of building type on electrical energy consumption differed across regions.
Variables with different influences across regions were identified using the MGWR model. The variables with differing influence across regions were winter temperature, green and water areas, and households with three or more members. This result did not appear in previous studies, and it could not be confirmed by the results of OLS analysis. Therefore, it is reasonable to use the MGWR model, which can reflect the spatial distribution characteristics of variables.
The present work is significant as a fundamental study for reducing the energy consumption of buildings by analyzing various factors that affect their electrical energy consumption. However, this study has a few limitations. First, this case study targeted Seoul, a city with an annual gross regional domestic product (GRDP) of 360 billion US dollars and one of the most active global economic centers. Moreover, Seoul is a densely populated metropolis with approximately 10 million inhabitants. In terms of natural environment, the city has large natural green areas, and Han River runs from east to west across the city. In addition, the built-up areas house numerous high-rise buildings. It is difficult to generalize the results of this study to all cities in the world because of the peculiar spatial characteristics of Seoul. Second, we did not consider indoor temperature, unlike previous studies, owing to data limitations. Because approximately 82% of the buildings in Seoul are made of reinforced concrete, we did not consider building materials as a variable. These are different from other previous studies in this study. Therefore, subsequent studies will be able to generate superior results by adopting a variety of variables depending on their unique situations.

Author Contributions

Conceptualization, H.J. and H.K.; methodology, H.J.; software, H.J.; validation, H.J.; formal analysis, H.J.; investigation, H.J.; resources, H.J.; data curation, H.J.; writing—original draft preparation, H.J.; writing—review and editing, H.K.; visualization, H.J.; supervision, H.K.; project administration, H.K.; All authors have read and agreed to the published version of the manuscript.


This work was supported by the research fund of Hanyang University (HY-2021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Flowchart of research methodology.
Figure 1. Flowchart of research methodology.
Buildings 12 00678 g001
Figure 2. Electrical energy consumption by administrative dong in Seoul in 2020.
Figure 2. Electrical energy consumption by administrative dong in Seoul in 2020.
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Figure 3. Data flow diagram of multiscale geographically weighted regression back-fitting calibration.
Figure 3. Data flow diagram of multiscale geographically weighted regression back-fitting calibration.
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Figure 4. Local R2 values of multiscale geographically weighted regression.
Figure 4. Local R2 values of multiscale geographically weighted regression.
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Figure 5. Multiscale geographically weighted regression results of local coefficients: (a) summer temperature; (b) winter temperature; (c) green and water area; (d) household income; (e) living population; (f) three-or-more-person household.
Figure 5. Multiscale geographically weighted regression results of local coefficients: (a) summer temperature; (b) winter temperature; (c) green and water area; (d) household income; (e) living population; (f) three-or-more-person household.
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Figure 6. Administrative boundary of Gu, and mountains and rivers of Seoul.
Figure 6. Administrative boundary of Gu, and mountains and rivers of Seoul.
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Figure 7. Multiscale geographically weighted regression results of local coefficients: (a) apartment; (b) detached house; (c) education; (d) average building age.
Figure 7. Multiscale geographically weighted regression results of local coefficients: (a) apartment; (b) detached house; (c) education; (d) average building age.
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Table 1. Definition of variables.
Table 1. Definition of variables.
Population and household factorsLiving populationTotal population of administrative dong estimated using public big data and communication dataSeoul Open Data Plaza
One-person householdNumber of households with one memberSeoul Commercial
Analysis Service
Two-person householdNumber of households with two members
Three-or-more-person householdNumber of households with three or more members
Socioeconomic factorsHousehold incomeAverage household income in administrative dong
Building characteristic factorsAverage number of floorsAverage number of floors in a buildingEAIS
(Electronic Architectural Administration
Information System)
Average building ageAverage number of years of a building
Apartment areaTotal floor area of an apartment
Detached house areaTotal floor area of a single house
Commercial building areaTotal floor area of a commercial building
Education building areaTotal floor area of an educational building
Office building areaTotal floor area of an official building
Environmental factorsSpring temperatureAverage air temperature in springMeteorological Agency in Korea
Summer temperatureAverage air temperature in summer
Fall temperatureAverage air temperature in fall
Winter temperatureAverage air temperature in winter
Green and water areasTotal area covered by vegetation and water bodies within an administrative dongEGIS
Geographic Information Service)
Table 2. Descriptive statistics of variables.
Table 2. Descriptive statistics of variables.
DivisionVariableMinimumMaximumMeanStandard Dev.VarianceVIF
DependentLog of building
electrical energy consumption
IndependentLiving population57106.001253928.00298939.89142864.4320410244485.883.593
One-person household115.0016971.004192.662457.346038503.792.968
Two-person household20.006152.002187.68881.79777547.922.318
Three-or-more-person household29.0011217.003875.651848.233415942.812.298
Household income2230710.006945812.003460789.901019302.251038977085862.022.005
Average number of floors2.0018.
Average building age5.0055.0028.086.2038.451.771
Apartment area527.0028482642.00803712.851707951.482917098242892.061.081
Detached house area0.00560122.00138281.46104137.3310844584450.762.384
Commercial building area2063.001340807.00161112.39148799.3822141254757.752.312
Education building area0.001766866.6783258.86156400.1824461015913.751.193
Office building area0.004472947.89156292.92404878.90163926920148.922.057
Spring temperature13.5821.2717.981.381.914.701
Summer temperature19.5628.5024.081.592.531.365
Fall temperature14.1519.9217.401.061.124.801
Winter temperature−1.562.751.240.670.451.451
Green cover and water areas548.243834250.60126512.09314705.5699039587845.581.220
Table 3. Analysis of spatial autocorrelation.
Table 3. Analysis of spatial autocorrelation.
Moran’s Index0.1153−0.0278
Expected Index−0.0023−0.0023
p-value0.000 00.17461
Table 4. Model fit summary of OLS, GWR, and MGWR models.
Table 4. Model fit summary of OLS, GWR, and MGWR models.
AICc899.06882. 88818.87
R20.5 550.6610.74 0
Adj. R20.5360.6070.685
No. of iteration--36
Table 5. Optimal bandwidth of each parameter in GWR and MGWR analyses.
Table 5. Optimal bandwidth of each parameter in GWR and MGWR analyses.
Division Bandwidth
VariableGWRMultiscale GWR
Population and household factorsLiving population264423
One-person household264423
Two-person household264423
Three-or-more-person household264103
Socioeconomic factorsHousehold income264423
Building characteristic factorsAverage number of floors264423
Average building age264418
Apartment area264314
Detached house area264423
Commercial building area264423
Education building area264145
Office building area264334
Environmental factorsSpring temperature264423
Summer temperature264235
Fall temperature264423
Winter temperature26478
Green and water areas264109
Table 6. Factors affecting the consumption of electrical energy in buildings: OLS and MGWR analysis results.
Table 6. Factors affecting the consumption of electrical energy in buildings: OLS and MGWR analysis results.
DivisionVariableOLSMultiscale GWR
-Intercept −0.0230.161−0.394 0.001 0.342
Population and household
Living population.0.388 *** 0.4110.005 0.399 0.411 0.421
One-person household0.036 0.0330.004 0.024 0.033 0.042
Two-person household−0.023−0.0270.013−0.054−0.022−0.009
Three-or-more-person household−0.011 0.0050.08−0.158−0.003 0.204
Socioeconomic factorsHousehold income0.156 *** 0.2150.001 0.212 0.216 0.217
characteristic factors
Average number of floors0.028−0.0240.01−0.044−0.025 0.002
Average building age0.030 0.0420.004 0.034 0.041 0.052
Apartment area0.107 *** 0.10.045 0.044 0.08 0.169
Detached house area−0.055−0.0770.009−0.086−0.082−0.053
Commercial building area0.066 0.0890.004 0.08 0.088 0.102
Education building area0.215 *** 0.1990.104−0.015 0.2 0.404
Office building area0.094 *0.140.02 0.098 0.144 0.169
Environmental factorsSpring Temperature0.031 0.0910.007 0.079 0.089 0.108
Summer Temperature0.111 *** 0.0710.071−0.043 0.054 0.225
Fall Temperature0.0150.010.014−0.006 0.003 0.039
Winter Temperature−0.106 ***−0.1010.119−0.481−0.086 0.163
Green and water areas0.076 * 0.1030.142−0.105 0.085 0.363
* p < 0.1, *** p < 0.01.
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Jo, H.; Kim, H. Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression. Buildings 2022, 12, 678.

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Jo H, Kim H. Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression. Buildings. 2022; 12(5):678.

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Jo, Hanghun, and Heungsoon Kim. 2022. "Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression" Buildings 12, no. 5: 678.

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