Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China
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DOI: 10.1016/j.jup.2017.01.004
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Cited by:
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- Kang, J. & Reiner, D., 2021.
"Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China,"
Cambridge Working Papers in Economics
2143, Faculty of Economics, University of Cambridge.
- Jieyi Kang & David Reiner, 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers EPRG2114, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
- Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
- Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
- Mario Flor & Sergio Herraiz & Ivan Contreras, 2021. "Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis," Energies, MDPI, vol. 14(20), pages 1-15, October.
- Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.
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Keywords
Residential electricity consumption; Smart power use; Smart-meter data;All these keywords.
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