基于经验模态分解与多分支神经网络的超短期风功率预测

Ultra-short-term wind power prediction based on empirical mode decomposition and multi-branch neural network

风功率预测是实现风电场监控及信息化管理的重要基础,风功率超短期预测常用于平衡负荷、优化调度,对预测精度有较高的要求。由于风电场环境复杂、风速不确定性因素较多,风功率时序信号往往具有非平稳性和随机性。循环神经网络(RNN)适用于时间序列任务,但无周期、非平稳的时序信号会增加网络学习的难度。为了克服非平稳信号在预测任务中的干扰,提高风功率预测精度,提出了一种结合经验模态分解与多分支神经网络的超短期风功率预测方法。首先将原始风功率时序信号通过经验模态分解(EMD)以重构数据张量,然后用卷积层和门控循环单元(GRU)层分别提取局部特征和趋势特征,最后通过特征融合与全连接层得到预测结果。在内蒙古某风场实测数据集上的实验结果表明,与差分整合移动平均自回归(ARIMA)模型相比,所提方法在预测精度方面有将近30%的提升,验证了所提方法的有效性。

Wind power prediction is an important basis for the monitoring and information management of wind farms.Ultra-short-term wind power prediction is often used to balance load and optimize scheduling and requires high prediction accuracy.Due to the complex environment of wind farm and many uncertainties of wind speed,the wind power time series signals are often non-stationary and random.Recurrent Neural Network(RNN)is suitable for time series tasks,but the nonperiodic and non-stationary time series signals will increase the difficulty of network learning.To overcome the interference of non-stationary signal in the prediction task and improve the prediction accuracy of wind power,an ultra-short-term wind power prediction method combining empirical model decomposition and multi-branch neural network was proposed.Firstly,the original wind power time series signal was decomposed by Empirical Mode Decomposition(EMD)to reconstruct the data tensor.Then,the convolution layer and Gated Recurrent Unit(GRU)layer were used to extract the local features and trend features respectively.Finally,the prediction results were obtained through feature fusion and full connection layer.Experimental results on the dataset of a wind farm in Inner Mongolia show that compared with AutoRegressive Integrated Moving Average(ARIMA)model,the proposed method improves the prediction accuracy by nearly 30%,which verifies the effectiveness of the proposed method.