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Domestic electricity use: A high-resolution energy demand model
Open accessAbstract
The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-min resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.
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Cites
Distribution System Modeling and Analysis
Kersting, W. (2012) · Electric Power Engineering Series · doi:10.1201/b12056-31 · Cited by 1,241
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Staffell, I., & Pfenninger, S. (2018) · Energy · doi:10.1016/j.energy.2017.12.051 · Cited by 415
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Ács, G., & Castelluccia, C. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24178-9_9 · Cited by 388
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Metadata
- Authors
- Richardson, I., Thomson, M., Infield, D., & Clifford, C.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 42
- Issue
- 10
- Pages
- 1878-1887
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2042569829
- Cited by
- 976
- References
- 1
- FWCI
- 27.66
- Type
- journal article
- Language
- en
- Open access
- green
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- Richardson, I., Thomson, M., Infield, D., & Clifford, C. (2010). Domestic electricity use: A high-resolution energy demand model. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2010.05.023
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.