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Is disaggregation the holy grail of energy efficiency? The case of electricity
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“Household energy disaggregation breaks down overall electricity consumption into energy use by individual appliances (see also Section 4.1) [33], which can help facilitate behavior change [772].”
From Tackling climate change with machine learning (2023) · cites this paper as [33]
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“A variety of ML techniques have been used to effectively disaggregate household energy, such as spectral clustering, Hidden Markov Models, and neural networks [33].”
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Metadata
- Authors
- Carrie Armel, K., Gupta, A., Shrimali, G., & Albert, A.
- Affiliations
- Indian School of Business (IN)
- Stanford University (US)
- Venue
- Energy Policy
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 52
- Pages
- 213-234
- ISSN
- 0301-4215 (ISSN-L)
- OpenAlex
- W2043279449
- Cited by
- 595
- References
- 78
- Citations by year
- 2026: 15 · 2025: 29 · 2024: 24 · 2023: 44 · 2022: 45 · 2021: 49 · 2020: 68 · 2019: 60 · 2018: 57 · 2017: 57 · 2016: 48 · 2015: 43 · 2014: 48 · 2013: 7
- FWCI
- 20.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy Policy (publishedVersion)
- Digital Eprints Services at ISB (DESI) (Indian School of Business) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Department of Energy ARPA-E (DE-AR0000018)
- California Energy Commission (PIR-10-054)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Keywords
- non-intrusive load monitoring, smart meters, residential energy efficiency, energy conservation, cost-effectiveness, building energy monitoring
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