Using clustering algorithms to characterise uncertain long-term decarbonisation pathways
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DOI: 10.1016/j.apenergy.2020.114947
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- Bjarnhedinn Gudlaugsson & Dana Abi Ghanem & Huda Dawood & Gobind Pillai & Michael Short, 2022. "A Qualitative Based Causal-Loop Diagram for Understanding Policy Design Challenges for a Sustainable Transition Pathway: The Case of Tees Valley Region, UK," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Kaiyan Wang & Haodong Du & Rong Jia & Hongtao Jia, 2022. "Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction," Sustainability, MDPI, vol. 14(19), pages 1-27, October.
- Domínguez, R. & Vitali, S., 2021. "Multi-chronological hierarchical clustering to solve capacity expansion problems with renewable sources," Energy, Elsevier, vol. 227(C).
- Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
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Keywords
Uncertainty; Decarbonisation pathways; Energy transition; Clustering analysis;All these keywords.
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