Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data
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DOI: 10.1016/j.energy.2021.122500
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Cited by:
- Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
- Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
- Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
- Yousaf Murtaza Rind & Muhammad Haseeb Raza & Muhammad Zubair & Muhammad Qasim Mehmood & Yehia Massoud, 2023. "Smart Energy Meters for Smart Grids, an Internet of Things Perspective," Energies, MDPI, vol. 16(4), pages 1-35, February.
- Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
- Cao, Xin & Liu, Chang & Wu, Mingxuan & Li, Zhi & Wang, Yihan & Wen, Zongguo, 2023. "Heterogeneity and connection in the spatial–temporal evolution trend of China’s energy consumption at provincial level," Applied Energy, Elsevier, vol. 336(C).
- Liu, Yixing & Liu, Bo & Guo, Xiaoyu & Xu, Yiqiao & Ding, Zhengtao, 2023. "Household profile identification for retailers based on personalized federated learning," Energy, Elsevier, vol. 275(C).
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Keywords
Consumption pattern; Socioeconomic; Smart meter; Clustering; Feature selection; Machine learning;All these keywords.
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