Offshore wind power ramp prediction based on optimal combination model

References

• Bin, Z., H. Duan, W. Qiuwei, H. Wang, S. Wing, K. Wing Chan, and YunfanMeng. 2021. Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network. International Journal of Electrical Power & Energy Systems 125:106411. doi:10.1016/j.ijepes.2020.106411.
   Web of Science ®Google Scholar
• Chen, D., J. Zhang, and S. Jiang. 2020. Forecasting the short-term metro ridership with seasonal and trend decomposition using loess and LSTM neural networks. IEEE Access 8:91181–87. doi:10.1109/ACCESS.2020.2995044.
   Web of Science ®Google Scholar
• Dhiman Harch, S., and D. Deb. 2021. Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting. International Transactions on Electrical Energy Systems 31 (9):e12818.
   Web of Science ®Google Scholar
• Dorado-Moreno, M., N. Navarin, P. A. Gutiérrez, L. Prieto, A. Sperduti, S. Salcedo-Sanz, and C. Hervás-Martínez. 2020. Multi-task learning for the prediction of wind power ramp events with deep neural networks. Neural Networks 123:401–11. doi:10.1016/j.neunet.2019.12.017.
   PubMed Web of Science ®Google Scholar
• Gaurav, D., and K. Vijay. 2019. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based Systems 165:169–96. doi:10.1016/j.knosys.2018.11.024.
   Web of Science ®Google Scholar
• Honghai, K., J. Wang, H. Zhang, Z. Hao, and G. Zhu. 2020. A new definition method of wind power ramp sections. IEEE Access 8:202058–66. doi:10.1109/ACCESS.2020.3032547.
   Web of Science ®Google Scholar
• Huitian, J., L. Han, and Z. Gao. 2021. Wind power creep prediction based on convolutional neural network feature extraction. Automation of Electric Power Systems 45 (4):98–105.
   Google Scholar
• Jiang, L., T. Song, B. Liu, M. Haotian, J. Chen, and Y. Cheng. 2020. Forecasting of wind capacity ramp events using typical event clustering identification. IEEE Access 8:176530–39. doi:10.1109/ACCESS.2020.3026864.
   Web of Science ®Google Scholar
• Lingling, L., M. T. ZhifengLiu, and M. K. L. KorbkulJantarakolica. 2021. Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power. Expert Systems with Applications 184:115579. doi:10.1016/j.eswa.2021.115579.
   Web of Science ®Google Scholar
• Maliha, H., and H. Raini. 2021. Prediction of agricultural emissions in Malaysia using the arima, LSTM, and regression models. International Journal on Perceptive and Cognitive Computing 7 (1):33–40.
   Google Scholar
• Mao, Y., and Y. Xinnan. 2022. Ultra-short-term combined forecasting considering power creep of wind farms. Journal of Northeastern Electric Power University 42 (1):63–70.
   Google Scholar
• Mingjian, C., J. Zhang, Q. Wang, V. Krishnan, and B. Mathias Hodge. 2017. A data-driven methodology for probabilistic wind power ramp forecasting. IEEE Transactions on Smart Grid 10 (2):1326–38.
   Web of Science ®Google Scholar
• Mohammed, G., Y. Zhang, X. Qian, and Z. Xing. 2021. Deterministic and probabilistic interval prediction for wind farm based on VMD and weighted LS-SVM. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 43 (7):800–14. doi:10.1080/15567036.2019.1632980.
   Web of Science ®Google Scholar
• Owolabi Taoreed, O., and A. Rahman Mohd Amiruddin. 2021. Prediction of band gap energy of doped graphitic carbon nitride using genetic algorithm-based support vector regression and extreme learning machine. Symmetry 13 (3):411–411. doi:10.3390/sym13030411.
   Google Scholar
• Tinghui, O., Z. Xiaoming, Q. Liang, H. Yusen, and Z. Tang. 2019. Prediction of wind power ramp events based on residual correction. Renewable Energy 136:781–92. doi:10.1016/j.renene.2019.01.049.
   Web of Science ®Google Scholar
• Wang, Q., Y. Zhipeng, Y. Rong, Z. Lin, and Y. Tang. 2019. An ordered curtailment strategy for offshore wind power under extreme weather conditions considering the resilience of the grid. IEEE Access 7:54824–33. doi:10.1109/ACCESS.2019.2911702.
   Web of Science ®Google Scholar
• Weinan, C., H. Zhijian, J. Yue, D. Yixing, and Q. Oi. 2021. Short-term load forecasting based on combined long- and short-term memory network and lightGBM model. Automation of Electric Power Systems 45 (4):91–97.
   Google Scholar
• Xuebo, J., W. Zheng, J. Kong, X. Wang, Y. Bai, S. Tingli, and S. Lin. 2021. Deep-learning forecasting method for electric power load via attention-based encoder-decoder with Bayesian optimization. Energies 14 (6):1596–1596. doi:10.3390/en14061596.
   Web of Science ®Google Scholar
• Yan, X., K. Yang, and G. Zhao. 2021. The influencing factors and hierarchical relationships of offshore wind power industry in China. Environmental Science and Pollution Research 28 (37):52329–44. doi:10.1007/s11356-021-14275-w.
   PubMed Web of Science ®Google Scholar
• Yinpeng, Q., X. Jian, J. Shangguang, Y. Liu, S. Yuanzhang, and K. Deping. 2021. Frequent pattern mining based modeling and forecasting for statistical characteristics of wind power ramp events. Automation of Electric Power Systems 45 (1):36–43.
   Google Scholar
• Yongxin, L., H. Wang, Z. Wang, and H. Wang. 2019. Research on wind power creep output power prediction system based on ISMC-PSO. Power System Protection and Control 47 (18):115–20.
   Google Scholar
• Zucatelli, P.J., E.G.S. Nascimento, A.Á.B. Santos, et al. 2021. An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay. Energy 230:120842. doi:10.1016/j.energy.2021.120842.
   Web of Science ®Google Scholar