Prepaid Electricity Plan and Electricity Consumption Behavior

Qiu, Yueming; Xing, Bo; Wang, Yi David

doi:10.1111/coep.12170

Cited by 19 publications

(5 citation statements)

References 33 publications

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“…Historic energy demand [33,34,37,39,68,71,82,89,90,101,107,108,111,131,147,163,175,178,229,236,263,285,331,340,346,349,356,361,365,396,398,425,442,449,478] Weather data [37,39,68,82,89,101,107,147,163,175,183,229,263,340,349,356,396,...…”

Section: Instance Basedmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Section: Instance Basedmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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“…Among them, E-23 and E-24 are non-dynamic rates (flat rates) while the rest are TOU rates. We drop households in the M-power program (E-24 plan), because E-24 is a prepaid electricity plan and provides consumers with extra information on usage through an in-home display and thus these consumers respond differently than consumers on other plans (Qiu et al, 2017c). The flat rate is an increasing block rate and its marginal electricity price does not differ by time of day.…”

Section: Tou Pricing Plans Of Salt River Projectmentioning

confidence: 99%

Time-of-Use Electricity Pricing and Residential Low-carbon Energy Technology Adoption

Liang¹,

Liu²,

Qiu³

et al. 2020

The Energy Journal

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This paper provides the first empirical evidence on the correlation between Time-Of-Use (TOU) electricity pricing and the adoption of energy efficient appliances and solar panels. We use household-level data in Phoenix, Arizona from an appliance saturation survey of about 16,000 customers conducted by a major electric utility. Our empirical results show that TOU consumers are associated with 27% higher likelihood to install solar panels but not more likely to adopt energy-efficient air conditioning based on the propensity score matching and coarsened exact matching methods. The findings highlight that policy makers could combine TOU and solar panels when implementing educational programs or when giving out financial incentives to consumers. Our results imply that TOU is associated with a similar impact of the incentive offered by $2,070∼$10,472 tax credits or rebates on solar adoption.

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“…A small number of pilot programs have been testing the introduction of prepaid metering in the United States (see Qiu et al (2016), for example).…”

Section: Prepaid Electricity Metersmentioning

confidence: 99%

“…Existing work on the effects of prepaid electricity metering is scarce, and consists largely of descriptive studies (Tewari and Shah 2003;Baptista 2013). A recent paper on the largest prepaid metering program in the United States, the Salt River Project, found reductions in consumption of around 12 percent per month after customers voluntarily switch to prepaid metering (Qiu et al 2016). The authors rely on a matching design to compare prepaid and postpaid customers, and do not calculate payoffs to the utility.…”

Section: Introductionmentioning

confidence: 99%

Charging Ahead: Prepaid Electricity Metering in South Africa

Jack¹,

Smith²

2016

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provided helpful comments. This RCT was registered in the American Economic Association Registry for randomized control trials under Trial number AEARCTR-0000582. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Prepaid Electricity Plan and Electricity Consumption Behavior

Cited by 19 publications

References 33 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

Time-of-Use Electricity Pricing and Residential Low-carbon Energy Technology Adoption

Charging Ahead: Prepaid Electricity Metering in South Africa

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