Ultra-Short-Term Wind Speed Forecasting Based on Meta Learning with Signal Trend and Fluctuation Decomposition

Zhengzhi Wang, Yongxin Su, Hui Li
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引用次数: 1

Abstract

Accurate wind speed prediction for each wind turbine is a critical basis of information for intelligent management and control of wind power system. How to precisely realize rapid prediction with small sample size is a critical open problem. This paper proposes an ultra-short-term wind speed prediction method based on meta learning algorithm with trend and fluctuation decomposition of wind speed signal. A meta learning prediction model with long short-term memory (LSTM) and recurrent neural network (RNN) is constructed as the base-learner, where low-frequency signal trend and high-frequency signal fluctuation are taken as the input of LSTM and RNN respectively. The forecasting results show that the mean absolute percentage error (MAPE) of the wind speed prediction scheme proposed in this paper is about 2.54%, and the sample size and training time costed in training are about 2.5% and 1.7% of traditional LSTM network. Results indicate that this method realizes ultra-short-term wind speed prediction with high accuracy as well as high efficiency.
基于信号趋势和波动分解元学习的超短期风速预报
每台风机的准确风速预测是风电系统智能化管理和控制的重要信息基础。如何在小样本量下精确实现快速预测是一个亟待解决的问题。提出了一种基于元学习算法的超短期风速预测方法,并对风速信号进行趋势和波动分解。构建以长短期记忆(LSTM)和递归神经网络(RNN)为基础学习器的元学习预测模型,其中低频信号趋势和高频信号波动分别作为LSTM和RNN的输入。预测结果表明,本文提出的风速预测方案的平均绝对百分比误差(MAPE)约为2.54%,训练所需的样本量和训练时间分别约为传统LSTM网络的2.5%和1.7%。结果表明,该方法实现了高精度、高效率的超短期风速预报。
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