Author:
Han Yulong,Guo Licai,He Haibo
Publisher
Springer Nature Singapore
Reference12 articles.
1. van Heerden L, Vermeulen H, van Staden C (2022) Wind power forecasting using hybrid recurrent neural networks with empirical mode decomposition. In: 2022 IEEE international conference on environment and electrical engineering and 2022 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe), Prague, Czech Republic, pp 1–6. https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854798
2. Bitar EY, Baeyens E, Khargonekar PP, Poolla K, Varaiya PL (2012) Optimal sharing of quantity risk for a coalition of wind power producers facing nodal prices. In: 2012 american control conference (ACC), Montreal, QC, Canada, pp 4438–4445. https://doi.org/10.1109/ACC.2012.6315524
3. Zhang B, Chen J, Wu W (2014) Autonomous-synergetic energy management system family for smart grids: concept, architecture and cases. Autom Electr Power Syst 38(09):6–14
4. Xue Y, Wang L et al (2019) An ultra-short-term wind power forecasting model combined with CNN and GRU networks. Renew Energy Res 37(03):456–462
5. Bossanyie A (1985) Short-term wind prediction using Kalman filters. Wind Eng 9(1):1–8