A Statistical Survey of the UK Residential Sector Electrical Loads

Tsagarakis, George; Collin, Adam J.; Kiprakis, Aristides

doi:10.1515/ijeeps-2013-0078

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…Modern loads are non-linear and are therefore more accurately represented by a mixture of the three general categories. The relationship describing this static non-linear load model is presented in Equation (1) for active power and in Equation (2) for reactive power (Tsagarakis et al, 2013).…”

Section: Electrical Load Modelingmentioning

confidence: 99%

5-Hz Distribution System for Mitigation of Energy Theft by Residential Consumers

Aminu

2020

Front. Energy Res.

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Energy theft through meter bypass by residential consumers constitutes significant revenue loss to electric power distribution companies. This unpleasant phenomenon is particularly worrisome in sub-Saharan Africa and some parts of Asia. This paper reports the outcome of a study that attempts to develop a modified distribution and metering system to demotivate energy theft by residential consumers. The project involves modeling and simulating a 5-Hz distribution system in SimPowerSystems. The developed system is then implemented using laboratory-scale power electronics components. The modified distribution system is thereafter interfaced with a modified digital consumer energy meter. The system is subsequently tested by deploying it to power two categories of lighting loads. The test results show that one category of lighting load flickers while the other category is non-operational whenever the proposed system is bypassed, though the power remains available. This is expected to demotivate the practice of energy theft through meter bypass. Thus, it is further expected that the proposed system will mitigate revenue loss by power distribution companies, thereby stimulating investments in the sub-sector.

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Section: Electrical Load Modelingmentioning

confidence: 99%

5-Hz Distribution System for Mitigation of Energy Theft by Residential Consumers

Aminu

2020

Front. Energy Res.

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“…Load profiles forecast the power variation over a period of time for an appliance, frequently as an average. For example, Tsagarakis et al 76 use aggregated load profiles, while Pipattanasomporn et al 77 use load profiles for specific machines. The former use a time-use survey (TUS) to construct the load profiles.…”

Section: Model For a Ieui : Set Of Inelastic Appliances With Uninterrmentioning

confidence: 99%

“…We minimize a weighted sum of comfort and cost (76), where the cost is given by (74) and the EV cost.…”

Section: Other Constraints and Objective Functionmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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Summary Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.

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“…A more finely-grained classification based on the electrical properties of appliances was proposed by Tsagarakis et al [13]. For example, the taxonomy proposed by Tsagarakis et al splits lighting into general incandescent lamps, fluorescent lamps and light-emitting diode (LED) sources.…”

Section: H Appliance Categorisationmentioning

confidence: 99%

Metadata for Energy Disaggregation

Kelly¹,

Knottenbelt²

2014

2014 IEEE 38th International Computer Software and Applications Conference Workshops

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Abstract-Energy disaggregation is the process of estimating the energy consumed by individual electrical appliances given only a time series of the whole-home power demand. Energy disaggregation researchers require datasets of the power demand from individual appliances and the whole-home power demand. Multiple such datasets have been released over the last few years but provide metadata in a disparate array of formats including CSV files and plain-text README files. At best, the lack of a standard metadata schema makes it unnecessarily time-consuming to write software to process multiple datasets and, at worse, the lack of a standard means that crucial information is simply absent from some datasets. We propose a metadata schema for representing appliances, meters, buildings, datasets, prior knowledge about appliances and appliance models. The schema is relational and provides a simple but powerful inheritance mechanism.

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A Statistical Survey of the UK Residential Sector Electrical Loads

Cited by 11 publications

References 11 publications

5-Hz Distribution System for Mitigation of Energy Theft by Residential Consumers

5-Hz Distribution System for Mitigation of Energy Theft by Residential Consumers

A realistic energy optimization model for smart‐home appliances

Metadata for Energy Disaggregation

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