Short-term wind power forecasting based on improved crow search algorithm and ESN neural network

被引：0

作者：

Ju Y. ^{[1
]}

Qi L. ^{[2
]}

Liu S. ^{[3
]}

机构：

[1] Henan Xinxiang Vocational and Technical College, Xinxiang

[2] Henan University of Urban Construction, Pingdingshan

[3] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Crow search algorithm (CSA); ESN neural network; Gauss function; Lévy flight; Wind power forecasting;

D O I：

10.7667/PSPC180251

中图分类号：

学科分类号：

摘要：

Accurate short-term wind power forecasting is important for improving the economic and stable operation of power system. Since it is easy to be affected by subjective factor and fall into local optimum in parameters selecting compared with traditional Neural Network, a novel combination forecasting approach based on Improved Crow Search Algorithm (ICSA) to optimize the parameters of Echo State Network (ESN) neural network is proposed to overcome above inadequacies. The Lévy flight is introduced to increase the searching efficiency at initial stages, and during the later stage of iteration, the Gauss function is added aiming at making an appropriate adjustments for the whole trajectory points after evolution, which can guarantee the ability of global optimization and successive approximation; it chooses optimal the weight values of the hidden layer to enhance the efficiency of neural network training by ICSA algorithm. Finally, effectiveness of the proposed forecasting model is tested on two groups of experimental data, the results show that proposed algorithm can effectively cope with the variability and intermittency of wind power time series, having higher modeling precision and faster convergence speed. © 2019, Power System Protection and Control Press. All right reserved.

引用

页码：58 / 64

页数：6

共 21 条

[1] Liu B., He Z., Jin H., A review of chaos theory in power system load prediction, Journal of Northeast Dianli University, 36, 2, pp. 7-13, (2016)
[2] Catalao J., Pousinho H., Mendes V., Hybrid intelligent approach for short-term wind power forecasting in Portugal, IET Renewable Power Generation, 5, 3, pp. 251-257, (2011)
[3] Jiang Y., Chen X., Yu K., Et al., Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm, Journal of Modern Power System and Clean Energy, 5, 1, pp. 126-133, (2017)
[4] Chen H., Wan Q., Wang Y., Short-term wind power forecast based on fat-tailed generalized autoregressive conditional heteroscedasticity in mean type model, Transactions of China Electrotechnical Society, 31, 5, pp. 91-98, (2016)
[5] Liu A., Xue Y., Hu J., Et al., Ultra-short- term wind power forecasting based on SVM optimized by GA, Power System Protection and Control, 43, 2, pp. 90-95, (2015)
[6] Zhang Y., Lu J., Meng Y., Et al., Wind power short-term forecasting based on empirial mode decomposition and chaotic phase space reconstruction, Automation of Electric Power Systems, 36, 5, pp. 24-28, (2012)
[7] Li Z., Ye L., Zhao Y., Et al., Short-term wind power prediction based on extreme learning machine with error correction, Protection and Control of Modern Power Systems, 1, 1, pp. 9-16, (2016)
[8] Shi B., Li Y., Yu X., Et al., Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model, Systems Engineering Theory and Practice, 30, 1, pp. 158-166, (2010)
[9] Li Y., Yuan Y., Guo T., Combination ultra-short-term prediction of wind power based on AMD-ICSA-SVM, Power System Protection and Control, 45, 14, pp. 113-120, (2017)
[10] Yin D., Sheng Y., Jiang M., Et al., Short-term wind speed forecasting using Elman neural network based on rough set theory and principal components analysis, Power System Protection and Control, 42, 11, pp. 46-50, (2014)

← 1 2 3 →