Coastal Wind Power Forecasting Research Scheme Design
DOI:
https://doi.org/10.54097/fcis.v2i1.2490Keywords:
Wind Power Forecasting, Renewable EnergyAbstract
As global resource and environmental problems become increasingly acute and climate change becomes more and more obvious, the large-scale development and utilization of new energy sources are highly valued by all countries in the world. Wind energy is highly valued because of its many advantages. Wind power, especially offshore wind power, has become one of the key development directions of renewable energy. To address the problem that coastal wind farms are in urgent need of accurate wind power forecasting, the authors of this paper investigate and summarize the relevant research work in this field. At the same time, the coastal meteorological micro-scale differences are obvious, and a research program for coastal wind farm power forecasting is designed to lay the foundation for subsequent specific research for the characteristics of this region.
Downloads
References
Qu Zongxi. Research on some difficult problems in wind speed combination prediction strategy [D]. Lanzhou University, 2019.
Ge Weichun, Sun Peng, Li Jiajue, et al. Robust estimation model for wind power prediction confidence under different scenarios during peak load hours of power grid[J]. High Voltage Technology, 2019, 45(04): 1281-1288.
Sun R.F., Zhang T., He Q., et al. A review of key technologies and applications for wind power prediction[J]. High Voltage Technology, 2021, 47(04): 1129-1143.
Wu, Dao-Lin. Research on short-term wind power forecasting methods [D]. Anhui University of Engineering, 2017.
Wang Y. S. Research on short-term wind power output power prediction based on deep learning [D]. Inner Mongolia Agricultural University, 2021.
DEMOLLI H, DOKUZ A S, ECEMIS A, et al. Wind power forecasting based on daily wind speed data using machine learning algorithms[J]. Energy Conversion and Management, 2019, 198: 111823.
Zhu Youcheng, Wang Jinrong, Xu Jian. A deep learning-based method for medium- and long-term wind power generation forecasting[J]. Guangdong Electric Power, 2021, 34(06): 72-78.
Huang F. Research on short-term wind power prediction method based on wind speed variation characteristics [D]. Nanjing University of Information Engineering, 2019.
Wang He. Short-term power prediction and optimization of grid-connected multi-objective scheduling for wind power [D]. Wuhan University, 2014.
Li, Y. Han. Research on short-term wind speed and wind power prediction based on wind farm historical data [D]. Lanzhou University of Technology, 2020.
WANG Y, HU Q, MENG D, et al. Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model[J]. Applied Energy, 2017, 208: 1097-1112.
ZHONG J, DONG G, SUN Y, et al. Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations[J]. Chinese Physics B, 2016, 25(11): 110502.
Lei, X.W.. Research on wind power day-ahead prediction and joint scheduling of power system based on deep neural network [D]. Xi'an University of Technology, 2018.
Zhang X. Short-term wind power forecasting based on dynamic modeling [D]. Shanghai Institute of Electrical Engineering, 2016.
YU R, GAO J, YU M, et al. LSTM-EFG for wind power forecasting based on sequential correlation features[J]. Future Generation Computer Systems, 2019, 93: 33-42.
SHAHID F, ZAMEER A, MUNEEB M. A novel genetic LSTM model for wind power forecast [J]. Energy, 2021, 223: 120069.
ZHANG K, QU Z, WANG J, et al. A novel hybrid approach based on cuckoo search optimization algorithm for short-term wind speed forecasting[J]. Environmental Progress & Sustainable Energy, 2017, 36(3): 943-952.
ALESSANDRINI S, DELLE MONACHE L, SPERATI S, et al. A novel application of an analog ensemble for short-term wind power forecasting[J]. Renewable Energy, 2015, 76: 768-781.
CHEN N, QIAN Z, NABNEY I T, et al. Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction[J]. IEEE Transactions on Power Systems, 2014, 29(2): 656-665.
Li L.L., Xu Y.H., Tian X.Y., et al. Short-term prediction of wind power based on combined model[J]. Journal of Electrical Engineering Technology, 2014, 29(S1): 475-480.
Ji X. Research on short-term wind power forecasting based on combinatorial analysis method [D]. North China University of Electric Power, 2019.
Gu Xingkai, Fan Gaofeng, Wang Xiaorong, et al. A review of wind power prediction techniques[J]. Power Grid Technology, 2007(S2): 335-338.
AHMED S D, AL-ISMAIL F S M, SHAFIULLAH M, et al. Grid Integration Challenges of Wind Energy: A Review[J]. IEEE Access, 2020, 8: 10857-10878.
Feng Shuanglei, Wang Weisheng, Liu Chun, et al. Research on physical methods for wind farm power prediction[J]. Chinese Journal of Electrical Engineering, 2010, 30(02): 1-6.
Yu Yixia. Research on short-term probabilistic power prediction method for wind power clusters with deep mining of spatio-temporal features [D]. Shandong University, 2020.
