Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
![Average electricity consumption per household by city and province of Korea in August 2018 [23].](publication/346247205/figure/tbl3/AS:1022313281626114@1620749888958/Average-electricity-consumption-per-household-by-city-and-province-of-Korea-in-August.png)
Source publication
![Residential electricity rates (low voltage) [8,16].](publication/346247205/figure/tbl1/AS:1022313281630217@1620749888921/Residential-electricity-rates-low-voltage-8-16_Q320.jpg)
![Electricity transaction results by energy sources in 2019 [12].](publication/346247205/figure/tbl2/AS:1022313281626113@1620749888939/Electricity-transaction-results-by-energy-sources-in-2019-12_Q320.jpg)
This paper aims to determine the relationship between residential electricity consumption and other factors by analyzing the correlation and multiple regression between residential electricity consumption and three variables which are known as the factors affecting residential electricity consumption. We used the electricity consumption, income, nu...
Context in source publication
Context 1
... recorded 39.6 • C, the highest temperature since 1907 when meteorological observations began, and Hongcheon recorded the highest temperature in Korea at 41.0 • C [22]. The average electricity consumption per household by city and province of Korea in August 2018 is as shown in Table 3. Seoul metropolitan city, 6 other metropolitan cities, Sejong self-governing city, and Gyeonggi-do used more than 300 kWh. ...Citations
... In addition, the findings shed light on the importance of awareness and attitudes toward climate change. People often do not recognize the link between their daily electricity consumption and climate change [94][95][96]. A feedback service can function as a useful campaign tool or platform by developing climate literacy and sharing important electricity conservation tips through messages [13,85]. ...
... The majority of the final energy consumption by domestic consumers was covered by energy generated • The number of people in the household. The larger the number, the higher the electricity consumption [10], since each person uses electrical equipment. • The composition of the family members. ...
With technological development and the decrease in the price of electrical appliances for domestic consumers, combined with users’ desire for a certain level of comfort, the number of electrical devices in homes has increased. Although current equipment is largely energy-efficient, the high number of these devices, used simultaneously, has led to an increase in electricity consumption. Electrical installation in homes has been replaced in most cases, but the connections have remained the same, without any checks being carried out in terms of load and operational safety. This paper presents an exploratory case study based on a questionnaire answered by 678 individuals from Romania. The objectives of the questionnaire were to identify the characteristics of the domestic consumer and their opinion on the state of the installation, energy costs, and energy consumption. Based on the analysis, 74.15% of the respondents stated that the power installed in their homes as domestic consumers increased. Based on the analysis conducted on-site, the risk of fire outbreaks in the power supply columns of apartment buildings was identified. To avoid critical situations, it is necessary to periodically check the connection and supply columns so that they are functional and safe to operate.
... Moreover, the socio-demographic characteristics of the household are also known to be a key factor in household energy consumption [16][17][18][19][20][21]. Ali et al. [16] examined the determinants of household energy consumption in Malaysia through a multiple regression analysis. ...
... The results showed that income had the largest correlation with energy consumption, followed by education level and household size. Other studies investigating the relationship between household socio-demographic characteristics and energy consumption, such as those by Chen et al. [17] and Kim [18], concluded that income and family size largely impacted household energy consumption. ...
Indonesian urban population increase has led to increased energy demands and housing inventory shortages. The Ministry of Public Works and Housing (PUPR) supplies collective housing for low-income communities (MBR). The development of low-carbon collective housing has been thought to suppress the abrupt increase in household-sector energy demand and lead to mitigated greenhouse gas (GHG) emissions. In tropical climates, it is essential to reduce the dependence on air conditioners (AC) to suppress energy consumption. Therefore, to investigate the relationship between cooling methods and energy consumption, this study surveyed the energy consumption per household and classified the existing cooling patterns of ACs, fans, and window openings in collective housing with different income groups in Indonesia. The results confirmed that the use of AC increases household energy consumption. Meanwhile, the implementation of natural ventilation (NV) showed significantly lower energy consumption with a high thermal satisfaction of more than 80% during the day and 90% at night; thus, both energy consumption reduction and indoor thermal comfort improvement could be achieved through these methods. The findings of this study serve as a starting point for verifying the energy saving effects of air conditioning habits with the consideration of socio-demographic changes for the purpose of decarbonizing collective housing, including future predictions and energy simulations.
... Overall, their study depicts the changes in gross, agricultural, industrial and services value additions due to the contribution of energy consumption. In South Korea, Kim (2020) considered the relationship between electricity consumption and other factors; income, number of household members, and age, using correlation and multiple regression. A significant correlation is demonstrated in the study while confirming that only income and the number of household members affecting on electricity consumption. ...
This research aims to develop a causal relationship model on political management for sustainability policy formation under Thai environmental law by applying the best and valid model with a non-spurious property called the Covariance-based on Structural Equation Model with exogenous variables (Covariance-based SEMxi Model). This newly-developed model is in distinction with any past models as it is made effectively applicable to any sectors across areas. The model can also be utilized to design a long-term forecasting model with the ability to determine appropriate future scenarios. When assessing the covariance-based SEMxi model performance, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are estimated at 1.19% and 1.30%, respectively, in comparison of other models, including Gray-Autoregressive Integrated Moving Average Model (GM-ARIMA), Gray Model (GM), Back Propagation Neural Network (BP), Artificial Neural Natural Model (ANN), and Multiple Regression Model (MR). As for the results, this research reveals a direct impact of economic factors on environmental and social factors. In the meanwhile, social factors have a direct impact on environmental and economic factors. The research also indicates a direct effect on the environment with a maximal magnitude of 67%. Whereas a direct effect of social factors on the environment is detected at the magnitude of 55%. These effects are perceived to exceed the specified carrying capacity set by Thailand. In addition, a causal relationship is observed between economic and social factors, where the environment is found with the lowest error correction capability of only 5%. At the same time, economic and social factors are noticed with greater correction capability of 59% and 31%, respectively. This finding implies that the ecosystem will experience slow recovery whenever it deteriorates. Hence, the government must place a higher concentration on the environment, while different measures on environmental legislation should be closely controlled to contain any future damage. Besides, energy consumption must be managed not to exceed the established carrying capacity by simultaneously implementing both proactive and reactive measures. This process can be strengthened by optimizing the newly-introduced model produced by this work for a scenario design in policy management to attain sustainability.
... Previous studies indicated that various factors such as socioeconomic and dwelling characteristics [52,53] could determine residential electricity consumption. Therefore, to understand the electricity usage among households in a growing city, the survey was designed to cover every aspect of users, including socioeconomic background, appliance ownership and housing characteristics, as shown in Figure 3. ...
Despite growing urban electricity consumption, information on actual energy use in the household sector is still limited and causal factors leading to electricity consumption remain speculative due to urban expansion and its growing complexity, particularly in developing countries such as Malaysia. This study aims to examine the critical determinants of household electricity consumption by evaluating the patterns and flows of consumption and analysing relationships and their effects on electricity usage among 620 urban households in Seremban, Malaysia. Results suggest that the average urban household electricity consumption is 648.31 kWh/month; this value continues to grow with the increase in the household monthly income (r = 0.360; p < 0.01) and number of rooms (r = 0.360; p < 0.01) as quality of life improves. A large portion of electricity is allocated for kitchen/home consumption, followed by cooling and lighting. Multiple linear regressions revealed that married households with a high monthly income and living in spacious houses together with three to five people are important predictors of electricity consumption in Seremban. This study empirically identified that the number of rooms is the most critical factor of electricity consumption and strategies to increase energy efficiency, maintain resource sustainability and minimise greenhouse gas threat on the urban ecosystem are vital. Therefore, promoting low carbon initiatives for energy conservation and technology improvement and implementing policies in the domestic sector are essential to achieve the greatest potential energy consumption reduction in urban regions.
... Previous studies indicated that various factors such as socioeconomic and dwelling characteristics [52,53] could determine residential electricity consumption. Therefore, to understand the electricity usage among households in a growing city, the survey was designed to cover every aspect of users, including socioeconomic background, appliance ownership and housing characteristics, as shown in Figure 3. ...
Despite growing urban electricity consumption, information on actual energy use in the household sector is still limited and causal factors leading to electricity consumption remain speculative due to urban expansion and its growing complexity, particularly in developing countries such as Malaysia. This study aims to examine the critical determinants of household electricity consumption by evaluating the patterns and flows of consumption and analysing relationships and their effects on electricity usage among 620 urban households in Seremban, Malaysia. Results suggest that the average urban household electricity consumption is 648.31 kWh/month; this value continues to grow with the increase in the household monthly income (r = 0.360; p < 0.01) and number of rooms (r = 0.360; p < 0.01) as quality of life improves. A large portion of electricity is allocated for kitchen/home consumption, followed by cooling and lighting. Multiple linear regressions revealed that married households with a high monthly income and living in spacious houses together with three to five people are important predictors of electricity consumption in Seremban. This study empirically identified that the number of rooms is the most critical factor of electricity consumption and strategies to increase energy efficiency, maintain resource sustainability and minimise greenhouse gas threat on the urban ecosystem are vital. Therefore, promoting low carbon initiatives for energy conservation and technology improvement and implementing policies in the domestic sector are essential to achieve the greatest potential energy consumption reduction in urban regions.
... Yoo et al., [11] studied that the residential electricity demands depend on various factors, such as the size of family, size of house, and household income. For the case of Seoul, Korea, Kim [12] also investigated effects of the factors on the electricity usage. The progressive rates of the electricity company and the electricity usages depending on the various factors of the apartment complexes can provide different electricity charges for the current two contracts. ...
For apartment complexes receiving medium-voltage electrical energies, the apartments can choose an electricity charging method between the single and general contracts in the Republic of Korea. In the single contract, a residential high-voltage rate is applied to the total electrical energy consumptions of households and common areas. On the other hand, in the general contract, different rate plans are applied to the electrical energy consumptions of households and their common areas, where a generic high-voltage rate plan is applied to the common consumption. Hence, depending on the amounts and composition of the consumptions, both contracts have their own strengths and weaknesses in terms of the total electricity charge. The management office of an apartment complex can select its preferred contract considering the amount and composition of the power consumptions on an annual basis. In this paper, we first formulate a model for the contracts and analyze their properties based on Monte-Carlo simulations. We then observe the contract properties through actual metering data from 30 apartment complexes in Korea. From the analysis of this paper, we can select appropriate contract for a given apartment complex and have guidelines for saving electricity charges. The greater the consumption of the electrical energy and the common area portion, the more advantageous the general contract is in terms of reducing electricity charges.
... Correlation coefficients are often used for load prediction or load profiles' determination [36][37][38]. Correlation may also be used to manage electrical properties of the grid, such as voltage levels, by distribution system operators (DSOs) [39], or to assess the impact of external factors such as income, age, and population density of customers on residential electricity consumption [40]. Correlation coefficients may also be used to assess the potential of demand side response programs to optimize development plans of generating units [41]. ...
Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.
Calculating trips from each traffic zone is one of the essential steps in the four-step model. Multiple linear regression (MLR) is the most popular among the various methods available for calculating trips. The main limitation of this method is its reliance on independent variables related to the zone. Due to the assumptions in this method, future predictions are also subject to the question of accuracy. Conversely, updating these independent variables requires additional time and resources for conducting selected types of surveys, such as home visit surveys (HVS). Using high-frequency data (HFD) that is freely available and is updated frequently, this paper estimates trip generation using fuzzy logic to fill in the gap. The fuzzy model was created using 2013 HVS data and updated the data with 2013 for validation purposes and 2019 as a prediction year. The research area chosen for this purpose is Thimbirigasyaya DSD in Western Province, Sri Lanka. According to the results of this study, a fuzzy rule-based model can be used when there are no exact data available, and the available high-frequency data shows a non-linear relationship with the dependent variable.
In the current progressive rate, the electricity bill is calculated based on the usage of electrical energy and corresponding progressive ranges. The time-of-use (TOU) electricity rate, in which the electricity bill is calculated based on different rates depending on the electrical energy used by season and time of day, is comparatively analyzed in this paper. For an experimental analysis, we used practical metering data of 593 households through an advanced metering infrastructure based on the progressive rate. Using the metering data, if we calculate the electricity bills based on the TOU rate, then 22.6% of the total number of billing showed reduced bills and 77.4% showed increased bills. Note that the households of the former case paid relatively high electricity bills based on the progressive rate. We then observe the pros and cons of the TOU rate comparing to the progressive rate through the experimental analysis in terms of the electricity bill. We could observe that the TOU rate is more advantageous than the progressive rate case when the annual electricity bill is higher than 704,000Won and the progressive rate is more advantageous than the TOU rate case when the annual electricity rate is less than 599,000Won.
