Household electricity demand forecast and energy savings potential for Vientiane, Lao PDR

Kleebrang, Wattanachai; Bunditsakulchai, Pongsun; Wangjiraniran, Weerin

doi:10.1080/14786451.2015.1017501

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…For example, Brounen et al [30] and Permana et al [31] study the relationship between gender and electricity consumption. Other studies looked into home size [32], presence of air conditioning/heating [33], [34], or appliances used at home [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

2022

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The residential sector is a major consumer of electricity, and its demand will rise by 65 percent by the end of 2050. The electricity consumption of a household is determined by various factors, e.g. house size, socio-economic status of the family, size of the family, etc. These factors play a critical role in analyzing the energy consumption causalities in the residential sector for better energy prediction models, effective price policy implementations, and improved customer engagement in energy efficiency programs. However, determining the effect of demographic factors on energy consumption is a challenging prospect. First, it is not trivial to study the causes of energy consumption variation, even for similar size residential houses, without analyzing the impact of interdependencies between demographic factors on energy consumption behavior. Second, to achieve higher accuracy of energy prediction models, it is necessary to identify key geodemographic factors that influence these models. Previous studies have only identified a limited number of socio-economic and dwelling factors. In this paper, we study the significance of 826 geodemographic factors on electricity consumption for 4917 homes in the City of London. Geodemographic factors cover a wide array of categories e.g. social, economic, dwelling, family structure, health, education, finance, occupation, and transport. Using Spearman correlation, we have identified 354 factors that are strongly correlated with electricity consumption. We also examine the impact of using geodemographic factors in designing forecasting models. In particular, we develop an encoderdecoder LSTM model which shows improved accuracy with geodemographic factors. We believe that our study will help energy companies design better energy management strategies. INDEX TERMSSocio-economic factors, geodemographic factors, electricity forecasting, encoder-decoder model

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Section: Literature Reviewmentioning

confidence: 99%

Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

2022

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“…Yet, they characterize as long-time horizon models with yearly data time split. For instance, Kleebrang et al [42] create a long-term model (2013-2030) for lighting and air conditioning replacement in Vientiane, Lao PDR, taking into account the residential and the economic sectors. Likewise, Pradhan et al [49] analyze the effects of electricity-based and bio-gas cooking from 2010 to 2050 in Nepal considering the same two sectors.…”

Section: Residential Sectormentioning

confidence: 99%

“…Aghamohamadi and Amjady [41] report the larger number of end-uses included in a single-sector model by incorporating three end-uses (e.g., A, L and SC). Likewise, Kleebrang et al [42] are the multi-sector leaders by comprising four end-uses within a model (e.g., A, L, SC, WH).…”

Section: Residential Sectormentioning

confidence: 99%

“…On the other hand, Radpour et al [45] create a long-term penetration model of energy-efficient appliances using econometric diffusion modeling that considers refrigerators, freezers, dishwashers, clothes washers, clothes dryers, and ranges. Kleebrang et al [42] design a long-term model to predict household electricity demand and energy-saving potential by appliance replacement in Vientianne using regression and scenario analyses. Finally, lighting end-use is studied by Heidari et al [48] using Material Flow Analysis and considering: legislation and evolution of lighting technology.…”

Section: Economic Dds Modelsmentioning

confidence: 99%

“…Although this model helps guide policy-makers in implementing subsidies, energy taxes, and codes and standards, it requires to be interpreted by an expert since it is not intuitive. In a second study, Kleebrang et al [42] use scenarios to present the result of retrofitting programs, considering the utilization of energy efficiency devices (e.g., regulatory measures). The model reveals that policy-makers should promote labeling programs (i.e., information instruments) to incentive energyefficiency attitudes.…”

Section: Market-basedmentioning

confidence: 99%

See 2 more Smart Citations

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

et al. 2021

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Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.

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Transitions towards energy-efficient appliances in urban households of Gujarat state, India

Garg

Maheshwari

Mukherjee

2020

International Journal of Sustainable Energy

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Household electricity demand forecast and energy savings potential for Vientiane, Lao PDR

Cited by 9 publications

References 11 publications

Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

Transitions towards energy-efficient appliances in urban households of Gujarat state, India

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