Ultra-short-term Wind Power Combined Prediction Based on CEEMD-SBO-LSSVR

被引：0

作者：

Zhou X. ^{[1
]}

Tong X. ^{[1
]}

机构：

[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu

来源：

Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 03期

关键词：

Combination model; Complementary ensemble empirical mode decomposition (CEEMD); Least squares support vector regression (LSSVR); The satin bower bird optimization algorithm (SBO); Ultra-short-term wind power prediction;

D O I：

10.13335/j.1000-3673.pst.2020.0584

中图分类号：

学科分类号：

摘要：

To improve the accuracy of wind power forecasting, an ultra-short-term wind power combined prediction based on the complementary ensemble empirical mode decomposition (CEEMD) the satin bower bird optimization algorithm (SBO) and the optimized least squares support vector regression (LSSVR) is proposed. With the random volatility of wind power sequences, the CEEMD is used to decompose the wind power data, and the decomposed series of components with different time characteristic scales are used as the training inputs for the LSSVR model. Then SBO is introduced to optimize the regularization parameter and the width of the kernel function of the LSSVR, and the wind power prediction model is established for each component. The final predicted values can be obtained by superimposing the prediction value of each component. The combined prediction based on the CEEMD-SBO-LSSVR not only effectively reduces the complexity of prediction, but also ensures the original wind power sequence's a small reconstruction error after the modal decomposition. The simulation results illustrate that this method has higher prediction accuracy for ultra-short-term wind power than other predictions. © 2021, Power System Technology Press. All right reserved.

引用

页码：855 / 862

页数：7

共 20 条

[1] (2011)
[2] Tascikaraoglu A, Uzunoglu M., A review of combined approaches for prediction of short-term wind speed and power, Renewable & Sustainable Energy Reviews, 34, pp. 243-254, (2014)
[3] Niu Dongxiao, Ji Huizheng, The method of error quantification analysis is introduced into the physical prediction model of wind power, Automation of Electric Power System, 44, 8, pp. 57-67, (2020)
[4] Ding Teng, Feng Donghan, Lin Xiaofan, Et al., Ultra-short term wind speed prediction based on modified ARIMA-GARCH model, Power System Technology, 41, 6, pp. 1808-1814, (2017)
[5] Khosravi A, Machado L, Nunes R O., Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil, Applied Energy, 224, pp. 550-566, (2018)
[6] Xiang Ling, Deng Zeqi, Zhao Yue, Multi-step wind speed prediction model based on LPF-VMD and KELM, Power System Technology, 43, 12, pp. 4461-4467, (2019)
[7] Yin Hao, Ou Zuhong, Chen De, Et al., An ultra-short-term wind power prediction based on two-layer mode decomposition and cascaded deep learning, Power System Technology, 44, 2, pp. 445-453, (2020)
[8] Cui Jia, Yang Junyou, Yang Lijian, Et al., A wind farm power prediction method based on improved CFD and wavelet hybrid neural network, Power System Technology, 41, 1, pp. 79-85, (2017)
[9] Wang Su, Jiang Xin, Zeng Liang, Et al., Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model, Power System Technology, 44, 3, pp. 917-926, (2020)
[10] Zhao Hui, Zhou Jie, Wang Hongjun, Et al., Short-term wind speed prediction based on CEEMDAN-PE and QGA-BP, Application of Electronic Technology, 44, 12, pp. 60-64, (2018)

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