A Review of Multi-temporal-and-spatial-scale Wind Power Forecasting Method

被引:0
|
作者
Jiang Z.-Y. [1 ]
Jia Q.-S. [1 ]
Guan X.-H. [1 ,2 ]
机构
[1] Department of Automation, Tsinghua University, Beijing National Research Center for Information Science and Technology, Beijing
[2] Key Laboratory of Intelligent Network and Network Security, Ministry of Education, Xi'an Jiao Tong University, Xi'an
来源
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 01期
基金
中国国家自然科学基金;
关键词
Forecasting method; Multi-scale; Spatial scale; Temporal scale; Wind power;
D O I
10.16383/j.aas.c180389
中图分类号
学科分类号
摘要
Wind power is one of the most installed renewable energy resources in the world, and the accuracy of wind power forecasting method directly affects dispatching and operation safety of the power grid. Since scheduling strategy of the power grid has multiple points and is relative to the geographical scope, this paper summarizes wind power forecasting methods from multi-temporal and multi-spatial perspective. Wind power forecasting usually focuses on specific spatial scale and temporal scale, and is finished with limited information, so this paper classifies researches from three aspects above. Firstly, this paper separates existing wind power forecasting methods into three spatial scales, namely a single wind turbine, a wind farm and a group of wind farms. In each spatial scale, we classify methods by whether using meteorological information, and afterwards by temporal scales. Lastly, in each temporal scale, we also summarize the challenges and achievements. This paper wishes researchers would find suitable methodology when dealing with different wind power forecasting tasks. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码:51 / 71
页数:20
相关论文
共 157 条
  • [31] Kavasseri R.G., Seetharaman K., Day-ahead wind speed forecasting using f-ARIMA models, Renewable Energy, 34, 5, pp. 1388-1393, (2009)
  • [32] Han L., Romero C.E., Yao Z., Wind power forecasting based on principle component phase space reconstruction, Renewable Energy, 81, pp. 737-744, (2015)
  • [33] Chen D.-H., Xue J.-S., An overview on recent progresses of the operational numerical weather prediction models, Acta Meteorologica Sinica, 62, 5, pp. 623-633, (2004)
  • [34] Wang J.-C., Yang P., Yang X., Research on wind power prediction modeling based on numerical weather prediction, Renewable Energy Resources, 31, 2, pp. 34-38, (2013)
  • [35] Li P., Guan X.H., Wu J., Aggregated wind power generation probabilistic forecasting based on particle filter, Energy Conversion and Management, 96, pp. 579-587, (2015)
  • [36] Khalid M., Savkin A.V., A method for short-term wind power prediction with multiple observation points, IEEE Transactions on Power Systems, 27, 2, pp. 579-586, (2012)
  • [37] Lazic L., Pejanovic G., Zivkovic M., Ilic L., Improved wind forecasts for wind power generation using the Eta model and MOS (model output statistics) method, Energy, 73, pp. 567-574, (2014)
  • [38] Peng H.W., Liu F.R., Yang X.F., A hybrid strategy of short term wind power prediction, Renewable Energy, 50, pp. 590-595, (2013)
  • [39] The interim measures on wind power forecasting management, Solar Energy, 14, pp. 6-7, (2011)
  • [40] Zhou X.-C., Lin W.-T., Lin Y.-H., Yao J.-S., Mo J.-Q., A method of solving a class of disturbed Lorenz system, Acta Physica Sinica, 60, 11, (2011)