Residential electricity consumption in Portugal: Findings from top-down and bottom-up models

Wiesmann, Daniel; Azevedo, Inês L.; Ferrão, Paulo; Fernández, John

doi:10.1016/j.enpol.2011.02.047

Cited by 165 publications

(118 citation statements)

References 18 publications

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“…Furthermore, high-income households have a higher share of indirect energy consumption due to more purchased goods (Cohen et al 2005). In contrast, others find no or only a weak effect on energy consumption (Abrahamse & Steg, 2009;Newton & Meyer, 2012) and electricity consumption (Sanquist et al, 2012;Thøgersen & Grønhøj, 2010;Wiesmann et al 2011), once other factors like household size and structure, dwelling type, floor area or appliance stock and use are controlled for.…”

Section: Contextual Factorsmentioning

confidence: 99%

“…Dwelling characteristics Among different dwelling characteristics, higher electricity consumption at the household level is predominantly related to larger home sizes (floor area) and the dwelling type of a detached house in comparison to apartment buildings (Statistics 2011;Thøgersen & Grønhøj, 2010;Wiesmann et al, 2011;Yohanis et al 2008). At the individual level, Sardianou (2007) identifies a positive effect of ownership and dwelling type (detached house), but no effect of floor area on the number of reported energy-conserving actions.…”

Section: Contextual Factorsmentioning

confidence: 99%

“…Previous studies point out that the direct effect of income on household electricity consumption decreases or becomes insignificant when the number of electric appliances is controlled for (e.g. Sanquist et al, 2012;Thøgersen & Grønhøj, 2010;Wiesmann et al, 2011).…”

Section: Contextual Factorsmentioning

confidence: 99%

See 2 more Smart Citations

Disentangling household and individual actors in explaining private electricity consumption

Seebauer

Wolf

2016

Energy Efficiency

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Previous research often regard household and individual as synonymous actors, although the overall household electricity consumption is the aggregate of diverging actions by individual household members. We disentangle the impact of actor-specific predictors on household and individual electricity consumption, employing regression models to data of 204 Austrian multi-person households. Predictors add more to the explained variance of household and individual electricity consumption if they are located at the same actor level as the dependent variable. While household electricity consumption is best predicted by the household context and value/knowledge factors, individual electricity consumption depends foremost on habit and whether a person stays at home during the day. The study exemplifies that future research and interventions need to decompose actor levels to better understand and target the drivers of private electricity consumption. Methodological challenges in measuring individual and household consumption behaviour are discussed.

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Section: Contextual Factorsmentioning

confidence: 99%

Section: Contextual Factorsmentioning

confidence: 99%

See 1 more Smart Citation

Disentangling household and individual actors in explaining private electricity consumption

Seebauer

Wolf

2016

Energy Efficiency

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“…Lucas et al [15] found that the family size, age and household time had impact on residential energy consumption. Druckman and Jackson, Kaza, Miah et al, and Wiesmann et al [1,5,[16][17]suggested that house type, location and other factors would inflence residential energy consumption. Ekholm et al [2] found that personal preference was one of the major factors that influence household energy consumption in India.…”

Section: （%）mentioning

confidence: 99%

Income, Consumption Tendency and Per Capital Electricity Consumption in the Age of China' Urbanization

Wen¹,

Chong-huai²,

Yao³

et al. 2017

Proceedings of the 3rd 2017 International Conference on Sustainable Development (ICSD 2017)

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Abstract. The rapid development of urbanization in China is coupled with the increase in per capital electricity consumption. This study focuses on the impacts of residents' income change and consumption tendency change on per capital electricity consumption in the process of urbanization in China. In this paper, LMDI method is used to analyze each factor's impact on per capital electricity consumption in the process of urbanization in China. The results show that income effect is the major determinant of per capital electricity consumption which is followed by population structure effect and electricity consumption effect; income effect in the urban regions is more than that in the rural regions; electricity consumption tendency effect in the rural regions is more than that in the urban regions.

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“…In the case of top-down models, the residential energy sector is regarded in its entirety without considering the energy consumption of individual customers. This type of model is especially useful to estimate price and income elasticities of the energy demand [4] and to study long-term macroeconomic trends [5]. In contrast, bottom-up models work on a disaggregated level with detailed data of energy end-uses like heating and lighting [6].…”

Section: Research Backgroundmentioning

confidence: 99%

Application-oriented modelling of domestic energy demand

Gruber

Jahromizadeh

Prodanović

et al. 2014

International Journal of Electrical Power & Energy Systems

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractDetailed residential energy consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain, including utilities, distributors and customers. The increasing interest in the residential consumption data is behind the roll-out of smart meters in large areas and led to intensified research efforts in new data acquisition technologies for the energy sector. This paper introduces a novel model for generation of residential energy consumption profiles based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The model takes into consideration a wide range of household appliances and its modular structure provides a high degree of flexibility. Residential consumption data generated by the proposed model are suitable for development of new services and applications such as residential real-time pricing schemes or tools for energy demand prediction. To demonstrate the main features of the model, an individual household consumption was created and the effects of a possible change in the user behaviour and the appliance configuration presented. In order to show the flexibility offered in creation of the aggregated demand, the detailed simulation results of an energy demand management algorithm applied to an aggregated user group are used.

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Residential electricity consumption in Portugal: Findings from top-down and bottom-up models

Cited by 165 publications

References 18 publications

Disentangling household and individual actors in explaining private electricity consumption

Disentangling household and individual actors in explaining private electricity consumption

Income, Consumption Tendency and Per Capital Electricity Consumption in the Age of China' Urbanization

Application-oriented modelling of domestic energy demand

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