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A review of electric load classification in smart grid environment
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“Using ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics (e.g., [66,116,166,261,861]), and thus predict who will be most amenable to new technologies and sustainable behavior change.”
From Tackling climate change with machine learning (2023) · cites this paper as [861]
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5 of the 72 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
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Metadata
- Authors
- Zhou, K.-L., Yang, S.-L., & Shen, C.
- Affiliations
- Hefei University of Technology (CN)
- ORCID
- Shen, Chao
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 24
- Pages
- 103-110
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2171208762
- Cited by
- 254
- References
- 72
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 13 · 2023: 19 · 2022: 28 · 2021: 30 · 2020: 28 · 2019: 31 · 2018: 28 · 2017: 18 · 2016: 18 · 2015: 18 · 2014: 8 · 2013: 1
- FWCI
- 9.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Keywords
- smart grid, clustering algorithms, bad data detection, load forecasting, tariff setting, fuzzy c-means clustering
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