Identifying trends in the use of domestic appliances from household electricity consumption measurements

Firth, Steven; Lomas, Kevin J.; Wright, A. J.; Wall, Rob

doi:10.1016/j.enbuild.2007.07.005

Cited by 265 publications

(143 citation statements)

References 7 publications

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“…Drawing on the idea of Firth et al [12], we see the electricity consumed by continuous appliances (e.g. security cameras, information displays and computer servers) and cold appliances (e.g.…”

Section: Modelling Rationalementioning

confidence: 99%

Modelling electricity consumption in office buildings: An agent based approach

Zhang

Siebers

Aickelin

2011

Energy and Buildings

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a b s t r a c tIn this paper, we develop an agent-based model which integrates four important elements, i.e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings. Based on a case study, we use this model to test the effectiveness of different electricity management strategies, and solve practical office electricity consumption problems. This paper theoretically contributes to an integration of the four elements involved in the complex organisational issue of office electricity consumption, and practically contributes to an application of an agent-based approach for office building electricity consumption study.

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Section: Modelling Rationalementioning

confidence: 99%

Modelling electricity consumption in office buildings: An agent based approach

Zhang

Siebers

Aickelin

2011

Energy and Buildings

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“…In addition, the effect of seasonal variation on the monthly power consumption was examined. In 2007, the power consumption of home appliances in 72 dwellings was monitored for over two years and the data was recorded in five-minute intervals [24]. The results of the study showed that the energy consumption increased by 4.5% in the second year due to an increase in the appliances that were left in standby mode.…”

Section: Introductionmentioning

confidence: 85%

The Determination of Load Profiles and Power Consumptions of Home Appliances

Issi

Kaplan

2018

Energies

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Abstract:In recent years, the increment of distributed electricity generation based on renewable energy sources and improvement of communication technologies have caused the development of next-generation power grids known as smart grids. The structures of smart grids have bidirectional communication capability and enable the connection of energy generated from distributed sources to any point on the grid. They also support consumers in energy efficiency by creating opportunities for management of power consumption. The information on power consumption and load profiles of home appliances is essential to perform load management in the dwelling accurately. In this study, the power consumption data for all the basic home appliances, utilized in a two-person family in Çankırı, Turkey, was obtained with high resolution in one-second intervals. The detailed power consumption analysis and load profile were executed for each home appliance. The obtained data is not only the average power consumption of each appliance but also characterizes different operating modes or their cycles. In addition, the impact of these devices on home energy management studies and their standby power consumptions were also discussed. The acquired data is an important source to determine the load profile of individual home appliances precisely in home energy management studies. Although the results of this study do not completely reflect the energy consumption behavior of the people who live in this region, they can reveal the trends in load demands based on a real sample and customer consumption behavior of a typical two-person family.

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“…In addition to studies where the focus has been price elasticity or forecasting electricity demand, research has also gone into other factors affecting electricity consumption. Firth et al (2008) identified trends between electricity consumption and appliance usage. Marvuglia and Messineo (2012) used artificial neural networks for short-term electricity forecasting in Italy and focused on how the use of air-conditioning affected electricity consumption.…”

Section: Introductionmentioning

confidence: 99%