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Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis

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  1. Ghaemi, Zahra & Tran, Thomas T.D. & Smith, Amanda D., 2022. "Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties," Applied Energy, Elsevier, vol. 321(C).
  2. Vering, Christian & Maier, Laura & Breuer, Katharina & Krützfeldt, Hannah & Streblow, Rita & Müller, Dirk, 2022. "Evaluating heat pump system design methods towards a sustainable heat supply in residential buildings," Applied Energy, Elsevier, vol. 308(C).
  3. Pinto, Edwin S. & Gronier, Timothé & Franquet, Erwin & Serra, Luis M., 2023. "Opportunities and economic assessment for a third-party delivering electricity, heat and cold to residential buildings," Energy, Elsevier, vol. 272(C).
  4. Lüth, Alexandra & Seifert, Paul E. & Egging-Bratseth, Ruud & Weibezahn, Jens, 2023. "How to connect energy islands: Trade-offs between hydrogen and electricity infrastructure," Applied Energy, Elsevier, vol. 341(C).
  5. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
  6. Liu, Aaron & Miller, Wendy & Cholette, Michael E. & Ledwich, Gerard & Crompton, Glenn & Li, Yong, 2021. "A multi-dimension clustering-based method for renewable energy investment planning," Renewable Energy, Elsevier, vol. 172(C), pages 651-666.
  7. Wirtz, Marco & Kivilip, Lukas & Remmen, Peter & Müller, Dirk, 2020. "5th Generation District Heating: A novel design approach based on mathematical optimization," Applied Energy, Elsevier, vol. 260(C).
  8. Thimet, P.J. & Mavromatidis, G., 2023. "What-where-when: Investigating the role of storage for the German electricity system transition," Applied Energy, Elsevier, vol. 351(C).
  9. Göke, Leonard & Kendziorski, Mario, 2022. "Adequacy of time-series reduction for renewable energy systems," Energy, Elsevier, vol. 238(PA).
  10. Kittel, Martin & Hobbie, Hannes & Dierstein, Constantin, 2022. "Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms," Energy, Elsevier, vol. 247(C).
  11. Yokoyama, Ryohei & Takeuchi, Kotaro & Shinano, Yuji & Wakui, Tetsuya, 2021. "Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method," Energy, Elsevier, vol. 228(C).
  12. Rigo-Mariani, Rémy, 2022. "Optimized time reduction models applied to power and energy systems planning – Comparison with existing methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  13. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
  14. Xia, Tian & Huang, Wujing & Lu, Xi & Zhang, Ning & Kang, Chongqing, 2020. "Planning district multiple energy systems considering year-round operation," Energy, Elsevier, vol. 213(C).
  15. Mavromatidis, Georgios & Petkov, Ivalin, 2021. "MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems," Applied Energy, Elsevier, vol. 288(C).
  16. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
  17. Li, Pei-Hao & Pye, Steve & Keppo, Ilkka, 2020. "Using clustering algorithms to characterise uncertain long-term decarbonisation pathways," Applied Energy, Elsevier, vol. 268(C).
  18. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
  19. Michael Stadler & Zack Pecenak & Patrick Mathiesen & Kelsey Fahy & Jan Kleissl, 2020. "Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects," Energies, MDPI, vol. 13(17), pages 1-24, August.
  20. Evženie Uglickich & Ivan Nagy & Dominika Vlčková, 2019. "Comparing clusterings using combination of the kappa statistic and entropy-based measure," METRON, Springer;Sapienza Università di Roma, vol. 77(3), pages 253-270, December.
  21. Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof & Allan, James & Hoffmann, Volker H., 2022. "MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits," Applied Energy, Elsevier, vol. 314(C).
  22. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
  23. Bohlayer, Markus & Bürger, Adrian & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2021. "Multi-period investment pathways - Modeling approaches to design distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 285(C).
  24. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).
  25. Yokoyama, Ryohei & Shinano, Yuji & Wakayama, Yuki & Wakui, Tetsuya, 2019. "Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method," Energy, Elsevier, vol. 181(C), pages 782-792.
  26. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
  27. Zatti, Matteo & Gabba, Marco & Freschini, Marco & Rossi, Michele & Gambarotta, Agostino & Morini, Mirko & Martelli, Emanuele, 2019. "k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization," Energy, Elsevier, vol. 181(C), pages 1051-1063.
  28. Richarz, Jan & Henn, Sarah & Osterhage, Tanja & Müller, Dirk, 2022. "Optimal scheduling of modernization measures for typical non-residential buildings," Energy, Elsevier, vol. 238(PA).
  29. Pinto, Edwin S. & Serra, Luis M. & Lázaro, Ana, 2020. "Evaluation of methods to select representative days for the optimization of polygeneration systems," Renewable Energy, Elsevier, vol. 151(C), pages 488-502.
  30. Gabrielli, Paolo & Fürer, Florian & Mavromatidis, Georgios & Mazzotti, Marco, 2019. "Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis," Applied Energy, Elsevier, vol. 238(C), pages 1192-1210.
  31. Kuepper, Lucas Elias & Teichgraeber, Holger & Baumgärtner, Nils & Bardow, André & Brandt, Adam R., 2022. "Wind data introduce error in time-series reduction for capacity expansion modelling," Energy, Elsevier, vol. 256(C).
  32. Pecenak, Zachary K. & Stadler, Michael & Mathiesen, Patrick & Fahy, Kelsey & Kleissl, Jan, 2020. "Robust design of microgrids using a hybrid minimum investment optimization," Applied Energy, Elsevier, vol. 276(C).
  33. Xu, Chuanbo & Wu, Yunna & Dai, Shuyu, 2020. "What are the critical barriers to the development of hydrogen refueling stations in China? A modified fuzzy DEMATEL approach," Energy Policy, Elsevier, vol. 142(C).
  34. Kittel, Martin & Hobbie, Hannes & Dierstein, Constantin, 2022. "Temporal aggregation of time series to identify typical hourly electricity system states: A systematic assessment of relevant cluster algorithms," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 247, pages 1-15.
  35. Hassan, Muhammed A. & Khalil, Adel & Abubakr, Mohamed, 2021. "Selection methodology of representative meteorological days for assessment of renewable energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 34-51.
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