A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features
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DOI: 10.1016/j.apenergy.2024.122905
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- Long, Sebastian & Marjanovic, Ognjen & Parisio, Alessandra, 2019. "Generalised control-oriented modelling framework for multi-energy systems," Applied Energy, Elsevier, vol. 235(C), pages 320-331.
- Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
- Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
- Krishna, Attoti Bharath & Abhyankar, Abhijit R., 2023. "Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method," Energy, Elsevier, vol. 265(C).
- Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
- Dong, Wei & Chen, Xianqing & Yang, Qiang, 2022. "Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability," Applied Energy, Elsevier, vol. 308(C).
- Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei & Xu, Zhao & McCulloch, Malcolm D. & Wong, Kit Po, 2017. "A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 439-451.
- Sun, Mingyang & Cremer, Jochen & Strbac, Goran, 2018. "A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 546-555.
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- Li, Guannan & Zhan, Lei & Fang, Xi & Gao, Jiajia & Xu, Chengliang & He, Xin & Deng, Jiahui & Xiong, Chenglong, 2024. "Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-info," Energy, Elsevier, vol. 312(C).
- Liuqing Gu & Jian Xu & Deping Ke & Youhan Deng & Xiaojun Hua & Yi Yu, 2024. "Short-Term Output Scenario Generation of Renewable Energy Using Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty," Sustainability, MDPI, vol. 16(24), pages 1-20, December.
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Keywords
Renewable scenario generation; Generative adversarial networks; Variational autoencoders; Mutual information; Interpretable feature;All these keywords.
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