A data-driven approach for discovering heat load patterns in district heating
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DOI: 10.1016/j.apenergy.2019.113409
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- Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
- Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
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- Shahrooz Abghari & Veselka Boeva & Jens Brage & Håkan Grahn, 2020. "A Higher Order Mining Approach for the Analysis of Real-World Datasets," Energies, MDPI, vol. 13(21), pages 1-23, November.
- Mengyao Lu & Guitao Xu & Jianjuan Yuan, 2023. "Installation Principle and Calculation Model of the Representative Indoor Temperature-Monitoring Points in Large-Scale Buildings," Energies, MDPI, vol. 16(17), pages 1-19, September.
- Huang, Yaohui & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng & Fu, Yonggang, 2024. "Sparse dynamic graph learning for district heat load forecasting," Applied Energy, Elsevier, vol. 371(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.
- Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
- Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Ma, Zhenjun, 2023. "A new data mining strategy for performance evaluation of a shared energy recovery system integrated with data centres and district heating networks," Energy, Elsevier, vol. 285(C).
- Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
- Rémi Delage & Toshihiko Nakata, 2022. "Multivariate Empirical Mode Decomposition and Recurrence Quantification for the Multiscale, Spatiotemporal Analysis of Electricity Demand—A Case Study of Japan," Energies, MDPI, vol. 15(17), pages 1-17, August.
- 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).
- Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
- Schaffer, Markus & Vera-Valdés, J. Eduardo & Marszal-Pomianowska, Anna, 2024. "Exploring smart heat meter data: A co-clustering driven approach to analyse the energy use of single-family houses," Applied Energy, Elsevier, vol. 371(C).
- Triebs, Merlin Sebastian & Tsatsaronis, George, 2022. "From heat demand to heat supply: How to obtain more accurate feed-in time series for district heating systems," Applied Energy, Elsevier, vol. 311(C).
- Vahid Arabzadeh & Peter D. Lund, 2020. "Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)," Energies, MDPI, vol. 13(9), pages 1-17, May.
- Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "Control algorithms to mitigate the effect of uncertainties in residential demand management," Applied Energy, Elsevier, vol. 306(PA).
- Sara Månsson & Marcus Thern & Per-Olof Johansson Kallioniemi & Kerstin Sernhed, 2021. "A Fault Handling Process for Faults in District Heating Customer Installations," Energies, MDPI, vol. 14(11), pages 1-18, May.
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Keywords
District heating; Energy efficiency; Heat load patterns; Clustering; Abnormal heat use;All these keywords.
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