Determinants of household electrical energy consumption: Evidences and suggestions with application to Montenegro

Đurišić, Vladimir; Rogić, Sunčica; Smolović, Julija Cerović; Radonjić, Milena

doi:10.1016/j.egyr.2019.10.039

Cited by 19 publications

(4 citation statements)

References 15 publications

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“…Increasing income by one SD is associated with a corresponding increase in HEC by 0.088 and 0.091 SDs for households with income up to 150k and above 150k, respectively. This outcome is consistent with past research [ [106] , [107] , [108] , [109] ]. Low-income individuals have lower HEC as they prioritise reducing living costs by limiting EC [ 19 , 110 ], though they cannot afford energy-efficient appliances.…”

Section: Discussionsupporting

confidence: 93%

Individuals’ contribution to household energy savings: The role of big-two personality traits

Shahin,

Ghasri,

Abbasi

2024

Heliyon

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Section: Discussionsupporting

confidence: 93%

Individuals’ contribution to household energy savings: The role of big-two personality traits

Shahin,

Ghasri,

Abbasi

2024

Heliyon

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“…First, among several determinants presented in the literature about the developing and newly industrialized (DNI) countries, the study identified that income, household size, and number of appliances are common factors used frequently to estimate household electricity consumption patterns. Our findings agree with the ones reported by [23][24][25] that income, household size, and number of appliances are common determinants of electricity consumption in DNI countries. Second, the result of load forecasting based on a univariate predictor variable show that the income had the lowest RMSE (0.8244 KWh) compared to household size (1.2314 KWh) and the number of electrical appliances (0.9868 KWh).…”

Section: Discussionsupporting

confidence: 93%

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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“…Table 1 summarises the factors identified by previous research and provides the Smartline survey, sensor or housing data used to create the covariate measures for inclusion in the analyses. For the homes used in the energy analyses, we also considered heating type [ 35 ], but it was not included as a covariate because all except six homes had gas-powered heating. The remaining six homes had air source heat pumps, and were included in the electricity analysis.…”

Section: Methodsmentioning

confidence: 99%

“…With regards to household expenditure, increased energy poverty is seen as an unintended consequence of COVID-19 that requires targeted financial support [ 33 ]. The impact is likely to be greater in socioeconomically disadvantaged areas, such as the area for our study, which can have lower energy consumption than other areas [ 34 , 35 ], and are already experiencing a greater impact of COVID-19 more generally in terms of mental health, education and digital inequality [ 36 , 37 , 38 ]. Evidence for meaningful differences in utility usage during lockdown is therefore important in understanding the wide range of impacts of the COVID-19 lockdown [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Changes in Domestic Energy and Water Usage during the UK COVID-19 Lockdown Using High-Resolution Temporal Data

Menneer

Taylor

et al. 2021

IJERPH

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In response to the COVID-19 outbreak, the UK Government provided public health advice to stay at home from 16 March 2020, followed by instruction to stay at home (full lockdown) from 24 March 2020. We use data with high temporal resolution from utility sensors installed in 280 homes across social housing in Cornwall, UK, to test for changes in domestic electricity, gas and water usage in response to government guidance. Gas usage increased by 20% following advice to stay at home, the week before full lockdown, although no difference was seen during full lockdown itself. During full lockdown, morning electricity usage shifted to later in the day, decreasing at 6 a.m. and increasing at midday. These changes in energy were echoed in water usage, with a 17% increase and a one-hour delay in peak morning usage. Changes were consistent with people getting up later, spending more time at home and washing more during full lockdown. Evidence for these changes was also observed in later lockdowns, but not between lockdowns. Our findings suggest more compliance with an enforced stay-at-home message than with advice. We discuss implications for socioeconomically disadvantaged households given the indication of inability to achieve increased energy needs during the pandemic.

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Determinants of household electrical energy consumption: Evidences and suggestions with application to Montenegro

Cited by 19 publications

References 15 publications

Individuals’ contribution to household energy savings: The role of big-two personality traits

Individuals’ contribution to household energy savings: The role of big-two personality traits

The effect of electricity consumption determinants in household load forecasting models

Changes in Domestic Energy and Water Usage during the UK COVID-19 Lockdown Using High-Resolution Temporal Data

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