Comparative analysis of prediction errors based on offshore wind power characteristics

被引：0
|
作者：
Yan J. [1 ,2 ]
Gao C. [1 ]
Yu G. [3 ]
机构：
[1] School of Economics and Management, Harbin University of Science and Technology, Harbin
[2] Science and Technology Office, Beijing University of Information Science and Technology, Beijing
[3] Yancheng Power Transmission and Intelligent Equipment Industry Research Institute, Harbin University of Science and Technology, Yancheng
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 03期
关键词：
Auto regressive integrated moving average; Improvement k-nearest neighbor; Prediction model; Wind power;
D O I：
10.13196/j.cims.2020.03.008
中图分类号：
学科分类号：
摘要：
Prediction of offshore wind power is a prerequisite for the stable operation of large-scale wind farms. The accuracy of wind power prediction plays an important role in improving the quality and consistency of the power grid.To improve the wind power prediction error, the Auto Regressive Integrated Moving Average(ARIMA)and the improved k-Nearest Neighbor(kNN)method were used to predict the offshore wind power. Results show that wind power could be predicted by different prediction techniques according to different characteristics, the prediction error was less than 20%, and the prediction accuracy could be improved by improving the prediction method. Finally, the improved two prediction methods were verified by a specific example, and the results were compared to verify the effectiveness of the algorithm. © 2020, Editorial Department of CIMS. All right reserved.
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页码：648 / 654
页数：6
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共 25 条
  • [1] Martin H., Henrik A., Torben S., Et al., Probing NWP model deficiencies by statistical postprocessing, Quarterly Journal of the Royal Meteorological Society, 142, 695, pp. 1017-1028, (2016)
  • [2] Vojtech B., Zbynek B., Petr Z., Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation, Atmospheric Research, 184, pp. 24-34, (2017)
  • [3] Robert M., Yelena L., Alan B., Et al., Evaluating and improving NWP forecast models for the future: How the needs of offshore wind energy can point the way, Bulletin of the American Meteorological Society, 99, 6, pp. 1155-1176, (2018)
  • [4] Munehiko Y., Junichi I., Hitoshi S., Et al., WGNE intercomparison of tropical cyclone forecasts by operational NWP models: A quarter century and beyond, Bulletin of the American Meteorological Society, 98, 11, pp. 2337-2349, (2017)
  • [5] Liang P., Lu J., Zhong W., Et al., Short-term prediction of wind power with non-iterative and time-phased optimality, Power System Technology, 39, 10, pp. 2766-2771, (2015)
  • [6] Yang J., Wang X., Luo X., Et al., The combination forecast of wind power intelligent based on numerical weather prediction and fuzzy clustering, Journal of Solar Energy, 38, 3, pp. 669-675, (2017)
  • [7] Wang B., Liu C., Zhang J., Et al., Uncertainty estimation of wind power prediction based on Monte-Carlo method, High Voltage Technology, 40, 10, pp. 3385-3391, (2015)
  • [8] Wang Q., Luo K., Yuan R., Et al., Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations, Energy, 166, pp. 1168-1180, (2019)
  • [9] Wei Y., Andre D., Sarahd, Et al., An operational application of NWP models in a wind power forecasting demonstration experiment, Wind Engineering, 38, 1, pp. 1-22, (2014)
  • [10] Joel I., Kazem A., Christopher D., Et al., Through-life engineering services of wind turbines, CIRP Journal of Manufacturing Science & Technology, 17, pp. 60-70, (2016)
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