基于小波包与回声状态网的风电功率预测

doi:10.11985/2021.03.017

摘要/Abstract

摘要：

为提高风电并网的稳定性、安全性,风电功率预测的准确性研究具有重要意义,提出一种基于小波包与回声状态网的风电功率预测方法,并给出了具体的应用步骤。通过小波包分解方法将历史的风速、风向、温度、湿度等气象因素以及输出功率数据进行分解,能够准确地反映历史数据的规律性;应用回声状态网对各个分解信号进行建模和预测,提高了建模的速度和准确性;最后,将各个分解信号的预测结果进行合成得到风电功率的预测值。

关键词: 风电功率预测, 小波包, 回声状态网, 分解信号

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

中图分类号:

TM744

李忠. 基于小波包与回声状态网的风电功率预测[J]. 电气工程学报, 2021, 16(3): 123-129.

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

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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