The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Is disaggregation the holy grail of energy efficiency? The case of electricity
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In the citing paper
“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]
In the citing paper
“A variety of ML techniques have been used to effectively disaggregate household energy, such as spectral clustering, Hidden Markov Models, and neural networks [33].”
From Tackling climate change with machine learning (2023) · cites this paper as [33]
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Citations
Cites
Self-efficacy mechanism in human agency
Bandura, A. (1982) · American Psychologist · doi:10.1037/0003-066x.37.2.122 · Cited by 15,975
Handbook of Industrial and Organizational Psychology.
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Handbook of industrial and organizational psychology
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Nonintrusive appliance load monitoring
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The Tipping Point: How Little Things Can Make a Big Difference
Meadows-Klue, D. (2004) · Interactive Marketing · doi:10.1057/palgrave.im.4340262 · Cited by 3,064
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Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation.
Bandura, A., & Schunk, D. H. (1981) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.41.3.586 · Cited by 2,675
Using Pedometers to Increase Physical Activity and Improve Health: A Systematic Review
Bravata, D. M., Smith-Spangler, C., Sundaram, V., Gienger, A. L., Lin, N., Lewis, R., Stave, C. D., Olkin, I., & Sirard, J. R. (2007) · JAMA · doi:10.1001/jama.298.19.2296 · Cited by 2,064
Feedback on household electricity consumption: a tool for saving energy?
Fischer, C. (2008) · Energy Efficiency · doi:10.1007/s12053-008-9009-7 · Cited by 1,337
Flagged first, then by citations
Research works printed but not linked: 9
- Darby, S., 2006. The Effectiveness of Feedback on Energy Consumption: A Review for DEFRA of the Literature on Metering, Billing and Direct Displays. Technical Report, Environmental Change Institute, University of Oxford, Oxford, England.
- Ehrhardt-Martinez, K., Donnelly, K.A., Laitner, J.A., 2010. Advanced Metering Initiatives and Residential Feedback Programs: A Meta-Review for Household Electricity-Saving Opportunities. Technical ReportsE105, American Council for an Energy-Efficient Economy, Washington, DC.
- Kolter, J.Z., & Jaakkola, T. Approximate Inference in Additive Factorial HMMs. In: The International Conference on Artificial Intelligence and Statistics, 2012. (to appear).
- Laitner, J., Ehrhardt-Martinez, K., McKinney, V., 2009. Examining the Scale of the Behaviour Energy Efficiency Continuum. American Council for an Energy-Efficient Economy, European Council for an Energy Efficient Economy Conference, Cote d'Azur, France, paper ID 1367.
- Maile, T., 2010. Comparing Measured and Simulated Building Energy Performance Data. Ph.D. Thesis. Department of Civil and Environmental Engineering, Stanford University, CA.
- Nakano, Y., Murata, H., Yoshimoto, K., Hidaka, S., Tadokoro, M., Nagasaka, K., 2006. Non-intrusive electric appliances load monitoring system using harmonic pattern recognition—performance test results at real households. Proceedings of the IEEE, 477–488.
- Nilsson, 2005, The Buildings Consume More Energy than Expected. Sustainable City of Tomorrow: B01—Experiences of a Swedish Housing Exposition, Formas, Stockholm, Sweden, pp. 107
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Cited by
Sorted by citations
Kelly, J., & Knottenbelt, W. J. (2015) · Scientific Data · doi:10.1038/sdata.2015.7 · Cited by 1,108
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey
Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012) · Sensors · doi:10.3390/s121216838 · Cited by 961
Smart Grids: A Cyber–Physical Systems Perspective
Yu, X., & Xue, Y. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2503119 · Cited by 726
Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey
Alahakoon, D., & Yu, X. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2414355 · Cited by 619
Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W. J., Rogers, A., Singh, A., & Srivastava, M. B. (2014) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/2602044.2602051 · Cited by 532
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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