A novel informer-time-series generative adversarial networks for day-ahead scenario generation of wind power
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DOI: 10.1016/j.apenergy.2024.123182
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- Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
- Qi, Yuchen & Hu, Wei & Dong, Yu & Fan, Yue & Dong, Ling & Xiao, Ming, 2020. "Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder," Applied Energy, Elsevier, vol. 274(C).
- Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
- Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Probabilistic solar power forecasting based on weather scenario generation," Applied Energy, Elsevier, vol. 266(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).
- Kong, Xiangyu & Xiao, Jie & Liu, Dehong & Wu, Jianzhong & Wang, Chengshan & Shen, Yu, 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties," Applied Energy, Elsevier, vol. 279(C).
- Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
- Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
- Zhang, Ji & Hu, Yuxin & Yuan, Yonggong & Yuan, Han & Mei, Ning, 2024. "Accuracy improvement of the load forecasting in the district heating system by the informer-based framework with the optimal step size selection," Energy, Elsevier, vol. 291(C).
- 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).
- Wang, Zhiwen & Shen, Chen & Liu, Feng, 2018. "A conditional model of wind power forecast errors and its application in scenario generation," Applied Energy, Elsevier, vol. 212(C), pages 771-785.
- 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).
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Keywords
Scenario generation; Wind power uncertainty; Time-series generative adversarial networks; Deep learning;All these keywords.
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