Wind Power Forecasting Based on Wavelet Packet and Echo State Network

doi:10.11985/2021.03.017

Abstract

Abstract:

In order to improve the stability and security of wind power integration, the accuracy of wind power prediction is of great significance. The method of wind power forecasting based on the wavelet packet and echo state network is proposed, and the specific application steps are given. Firstly, the historical data of meteorological factors such as wind speed, wind direction, temperature, humidity and wind power is decomposed by wavelet packet decomposition, which can accurately reflect the regularity of historical data. Secondly, the echo state network is applied to model and predict each decomposed signal, which improves the speed and accuracy of modeling. Finally, the prediction results of each decomposed signal are synthesized to obtain the predicted value of wind power.

Key words: Wind power prediction, wavelet packet, echo state network, each decomposed signal

CLC Number:

TM744

LI Zhong. Wind Power Forecasting Based on Wavelet Packet and Echo State Network[J]. Journal of Electrical Engineering, 2021, 16(3): 123-129.

Figures/Tables 9

References 21

[1]	钱政, 裴岩, 曹利宵, 等. 风电功率预测方法综述[J]. 高电压技术, 2016, 42(4):1047-1060.
	QIAN Zheng, PEI Yan, CAO Lixiao, et al. Review of wind power forecasting method[J]. High Voltage Engineering, 2016, 42(4):1047-1060.
[2]	AHMED A, KHALID M. A review on the selected applications of forecasting models in renewable power systems[J]. Renewable and Sustainable Energy Reviews, 2019(100):9-21.
[3]	YANG Bo, ZHONG Linen, WANG Jingbo, et al. State-of-the-art one-stop handbook on wind forecasting technologies:An overview of classifications,methodologies,and analysis[J]. Journal of Cleaner Production, 2021(283):124628.
[4]	KARINIOTAKIS G N, STAVRAKAKIS G S, NOGARET E F. Wind power forecasting using advanced neural networks models[J]. IEEE Transactions on Energy Conversion, 1996, 11(4):762-767. doi: 10.1109/60.556376
[5]	王丽婕, 厉虹, 方市彬. 基于人工鱼群优化算法的支持向量机短期风电功率预测模型[J]. 电气工程学报, 2016, 11(10):7-12.
	WANG Lijie, LI Hong, FANG Shibin. A short-term wind electric power prediction model of support vector machine based on artificial fish population optimization algorithm[J]. Journal of Electrical Engineering, 2016, 11(10):7-12.
[6]	MOHANDES M A, HALAWAN T O, REHMAN S, et al. Support vector machines for wind speed prediction[J]. Renewable Energy, 2004, 29(6):939-947. doi: 10.1016/j.renene.2003.11.009
[7]	FOLEY A M, LEAHY P G, MARVUGLIA A, et al. Current methods and advances in forecasting of wind power generation[J]. Renewable Energy, 2012, 37(1):1-8. doi: 10.1016/j.renene.2011.05.033
[8]	LOUKA P, GALANIS G, SIEBERT N, et al. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering[J]. Journal of Wind Engineering & Industrial Aerodynamics, 2008, 96(12):2348-2362.
[9]	INGRID D. 小波十讲[M]. 北京: 国防工业出版社, 2004.
	INGRID D. Ten lectures on wavelets[M]. Beijing: National Defense Industry Press, 2004.
[10]	韩晓娟, 陈跃燕, 张浩, 等. 基于小波包分解的混合储能技术在平抑风电场功率波动中的应用[J]. 中国电机工程学报, 2013, 33(19):8-13.
	HAN Xiaojuan, CHEN Yueyan, ZHANG Hao, et al. Application of hybrid energy storage technology based on wavelet packet decomposition in smoothing the fluctuation of wind power[J]. Proceedings of the CSEE, 2013, 33(19):8-13.
[11]	RUIZ-AGUILAR J J, TURIAS I, GONZALEZ-ENRIQ UE J, et al. A permutation entropy-based EMD-ANN forecasting ensemble approach for wind speed prediction[J]. Neural Computing and Applications, 2021, 33(7):2369-2391. doi: 10.1007/s00521-020-05141-w
[12]	LIU Hui, TIAN Hongqi, LI Yanfei. Comparison of new hybrid FEEMD-MLP,FEEMD-ANFIS,wavelet packet-MLP and wavelet packet-ANFIS for wind speed predictions[J]. Energy Conversion and Management, 2015, 89,1-11. doi: 10.1016/j.enconman.2014.09.060
[13]	马晓博. 基于小波变换和BP神经网络的短期风电功率预测[J]. 电力科学与技术学报, 2015, 30(2):92-97.
	MA Xiaobo. Short-term wind power prediction based on wavelet analysis and BP neural network[J]. Journal of Electric Power Science and Technology, 2015, 30(2):92-97.
[14]	肖迁, 李文华, 李志刚, 等. 基于改进的小波-BP神经网络的风速和风电功率预测[J]. 电力系统保护与控制, 2014, 42(15):80-86.
	XIAO Qian, LI Wenhua, LI Zhigang, et al. Wind speed and power prediction based on improved wavelet-BP neural network[J]. Power System Protection and Control, 2014, 42(15):80-86.
[15]	叶小岭, 刘波, 邓华, 等. 基于小波分析和PSO优化神经网络的短期风电功率预测[J]. 可再生能源, 2014, 32(10):1486-1492.
	YE Xiaoling, LIU Bo, DENG Hua, et al. Short-term wind power prediction based on wavelet analysis and PSO optimized neural network[J]. Renewable Energy Resources, 2014, 32(10):1486-1492.
[16]	叶瑞丽, 郭志忠, 刘瑞叶, 等. 基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测[J]. 电工技术学报, 2017, 32(21):103-111.
	YE Ruili, GUO Zhizhong, LIU Ruiye, et al. Wind speed and wind power forecasting method based on wavelet packet decomposition and improved Elman neural network[J]. Transactions of China Electrotechnical Society, 2017, 32(21):103-111.
[17]	曹青. 并网型光伏发电短期功率预测与优化配置研究[D]. 合肥:安徽工程大学, 2019.
	CAO Qing. Study on short-term power prediction and optimal configuration of grid-connected photovoltaic power generation[D]. Hefei:Anhui Polytechnic University, 2019.
[18]	杨德全. 基于神经网络的光伏发电系统发电功率预测[D]. 北京:华北电力大学, 2014.
	YANG Dequan. Generation forecasting for photovoltaic system based on artificial neural networks[D]. Beijing:North China Electric Power University, 2014.
[19]	LIU H, TIAN H Q, LI Y F. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms[J]. Energy Conversion & Management, 2015, 100(16):16-22.
[20]	杨茂, 杨宇. 基于小波包与LSSVM的短期光伏输出功率预测研究[J]. 可再生能源, 2019, 37(11):1595-1602.
	YANG Mao, YANG Yu. Short-term photovoltaic output power prediction based on wavelet packet and LSSVM[J]. Renewable Energy Resources, 2019, 37(11):1595-1602.
[21]	JAEGER H. The “Echo State” approach to analyze and training recurrent neural networks[R]. Technical Report 148,GMD-Frauhofer Institute for Autonomous Intelligent Systems, 2001.

预测方法	MAE/kW	RMSE/kW	ME/kW
小波包-ESM	2.31	2.88	1.73
小波包-BP 神经网络	3.86	4.67	3.57
小波-支持向量机	4.49	5.45	4.38