Determinants of energy expenditures for Turkish households using quantile regression and data from an original survey in Turkey

Karaaslan, Kübranur Çebi; Algül, Yahya

doi:10.1007/s11356-022-24323-8

Cited by 3 publications

(2 citation statements)

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“…A recent study by [8] explores the influencing factor for urban household electricity consumption and found that household income, appliances, and per capita electricity consumption are the main determinants of urban household electricity consumption. Another study by [9] presents socioeconomic indicators such as age, gender, marital status, income, and background education as the critical electrical consumption determinants for Turkish households. The two studies conducted by [10,11] indicate that household income and electricity price are the major determinants of household electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

“…The two studies conducted by [10,11] indicate that household income and electricity price are the major determinants of household electricity consumption. Several other researchers from DNI countries presented the electricity determinants in their respective countries, such as Ghana [12], Turkey [9], Nigeria [13], and India [14], to mention a few.…”

Section: Introductionmentioning

confidence: 99%

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The effect of electricity consumption determinants in household load forecasting models

Bakiri,

Mbembati

2023

Journal of Electrical Systems and Inf Technol

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Usually, household electricity consumption fluctuates, often driven by several electrical consumption determinants such as income, household size, and price. Recently, research studies on the investigation of predictor variables in household electricity consumption have increased especially in the developing and newly industrialized countries. However, the studies just focus on identifying the predictor variables of household electricity consumption that influence load forecasting models. In Tanzania, for instance, scholars found that using the “income” determinant improves the performance of a forecasting model. The scholars suggest without any empirical bases that adding more predictor variables would have improved the accuracy of the model. This study aims to analyze the effect of the number of predictor variables on household load forecasting performance based on Tanzania’s data. Nonlinear regression based on a Weibull function and multivariate adaptive regression splines approaches are used for this purpose. Our findings indicate that income, household size, and number of appliances are common predictor variables of household consumption in developing countries. The measured forecasting root-mean-square error (RMSE) when using income, household size, and the number of appliances is 0.8244, 1.2314, and 0.9868, respectively. Finally, we forecasted load using all three determinants and the RMSE dropped to 0.7031. Having obtained the smaller value of RMSE when all predictors are used reveals that the inclusion of all three predictor variables in load forecasting leads to a significant decrease in RMSE by 14.73%. Therefore, the study recommends using multiple predictor variables in load forecasting models to increase accuracy.

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