The determinants of household electricity consumption in Taiwan: Evidence from quantile regression

Huang, Wen‐Nan

doi:10.1016/j.energy.2015.04.101

Cited by 163 publications

(89 citation statements)

References 53 publications

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“…Huang () showed that household income, number of household members, and number of elderly members in the household affected power consumption. Also, that a larger household size, homes with more appliances, and owner‐occupied, business‐using, and multifloor houses contributed to higher household electricity consumption.…”

Section: Review Of Literaturementioning

confidence: 99%

“…McLoughlin, Duffy, and Conlon (2012) stated that the most frequently mentioned factors were the household's income, number of electrical devices, number of household members, and type of house. Huang (2015) showed that household income, number of household members, and number of elderly members in the household affected power consumption. Also, that a larger household size, homes with more appliances, and owner-occupied, business-using, and multifloor houses contributed to higher household electricity consumption.…”

Section: Electricity Consumptionmentioning

confidence: 99%

“…Based on these studies, five factors are usually considered as independent variables affecting electricity consumption. The first factor is economic characteristics such as monthly household income (Huang, 2015;McLoughlin et al, 2012). The second factor is demographic characteristics such as number of household members, number of elders in the household, and age of the household head (Huang, 2015;Kavousian et al, 2013;McLoughlin et al, 2012;Yohanis, 2011).…”

Section: Electricity Consumptionmentioning

confidence: 99%

“…The first factor is economic characteristics such as monthly household income (Huang, 2015;McLoughlin et al, 2012). The second factor is demographic characteristics such as number of household members, number of elders in the household, and age of the household head (Huang, 2015;Kavousian et al, 2013;McLoughlin et al, 2012;Yohanis, 2011). The third factor is physical characteristics such as type of house, location of house, housing area, and type of heating (Huang, 2015;McLoughlin et al, 2012;Mondol et al, 2007).…”

Section: Electricity Consumptionmentioning

confidence: 99%

“…The second factor is demographic characteristics such as number of household members, number of elders in the household, and age of the household head (Huang, 2015;Kavousian et al, 2013;McLoughlin et al, 2012;Yohanis, 2011). The third factor is physical characteristics such as type of house, location of house, housing area, and type of heating (Huang, 2015;McLoughlin et al, 2012;Mondol et al, 2007). The fourth factor is power consumption characteristics such as number of electrical appliances, efficiency, and time at home (Kavousian et al, 2013;McLoughlin et al, 2012;Mondol et al, 2007;Yohanis, 2011).…”

Section: Electricity Consumptionmentioning

confidence: 99%

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Electricity Consumption by Age of Single‐Person Households in Korea

Kim

2018

Family & Consum Sci Res J

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This study analyzed the electricity consumption of single‐person households in Korea and examined whether the determinants of electricity consumption varied by age. The study analyzed the physical characteristics, demographic and economic characteristics, and electricity consumption characteristics. The sample included 1,035 single‐person households; it was drawn from the 2014 Korea Energy Consumption Survey. Descriptive statistics revealed differences in most characteristics by age. The results of multiple regression showed that there were no common factors affecting average monthly power consumption in all age groups. However, two factors, average monthly income and the number of heating devices, had the greatest effect on electricity consumption for elderly single‐person households.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Electricity Consumptionmentioning

confidence: 99%

Section: Electricity Consumptionmentioning

confidence: 99%

Section: Electricity Consumptionmentioning

confidence: 99%

Section: Electricity Consumptionmentioning

confidence: 99%

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Electricity Consumption by Age of Single‐Person Households in Korea

Kim

2018

Family & Consum Sci Res J

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Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

Chou

Hsu

2021

Intl J of Energy Research

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The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic webbased system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.

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