Occupancy detection of residential buildings using smart meter data: A large-scale study
Created by W.Langdon from
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- @Article{RAZAVI:2019:EB,
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author = "Rouzbeh Razavi and Amin Gharipour and
Martin Fleury and Ikpe Justice Akpan",
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title = "Occupancy detection of residential buildings using
smart meter data: A large-scale study",
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journal = "Energy and Buildings",
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year = "2019",
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volume = "183",
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pages = "195--208",
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keywords = "genetic algorithms, genetic programming, Buildings
occupancy, Smart meters, Privacy, Data mining",
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ISSN = "0378-7788",
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URL = "http://www.sciencedirect.com/science/article/pii/S0378778818316724",
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DOI = "doi:10.1016/j.enbuild.2018.11.025",
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abstract = "Advanced Metering Infrastructures (AMIs) are installed
to gather localized and frequently acquired energy
consumption data. Despite many potential benefits, the
installation of such meters has resulted in growing
privacy concerns amongst the public. By analyzing the
electricity consumption behavior of more than 5000
households over an 18-month period and deploying a wide
array of machine learning methods, this paper examines
whether high-frequency meter data are sufficient to
predict the home-occupancy status of households not
only in the present but also in the future. The authors
believe that this is the first study at such a scale on
this issue. The study proposes a genetic programming
approach for feature engineering when training the
models. The results reveal a high predictive power for
smart meter data in establishing the present and future
occupancy status of households. Also, the analysis of
the demographic data suggests that households known to
be least concerned with privacy are the ones who are
more vulnerable to smart meter privacy implications",
- }
Genetic Programming entries for
Rouzbeh Razavi
Amin Gharipour
Martin Fleury
Ikpe Justice Akpan
Citations