New methods for clustering district heating users based on consumption patterns
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DOI: 10.1016/j.apenergy.2019.113373
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Cited by:
- Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Lu, Xuan & Zhao, Laifu & Zhao, Yan & Feng, Yongtao, 2024. "Analyzing daily change patterns of indoor temperature in district heating systems: A clustering and regression approach," Applied Energy, Elsevier, vol. 358(C).
- Andre Beblek & Florian Felix Sehr & Viktor Grinewitschus & Carolin Baedeker & Aaron Immanuel Wolber, 2024. "Development and Application of a Platform for Optimising Heating System Operation Based on the Building User’s Temperature Perception," Energies, MDPI, vol. 17(17), pages 1-23, September.
- Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
- Anders Rhiger Hansen & Daniel Leiria & Hicham Johra & Anna Marszal-Pomianowska, 2022. "Who Produces the Peaks? Household Variation in Peak Energy Demand for Space Heating and Domestic Hot Water," Energies, MDPI, vol. 15(24), pages 1-23, December.
- 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).
- Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
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Keywords
District heating; User clustering; Energy consumption pattern; Feature extraction;All these keywords.
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