基于W-BiLSTM的可再生能源超短期发电功率预测方法

针对现有新能源超短期预测方法难以有效挖掘和分析数据的固有波动规律,且当时序过长时易丢失重要信息等问题,提出了一种基于注意力(Attention)机制的小波分解-双向长短时记忆网络(W-BiLSTM)超短期风、光发电功率预测方法.首先,利用小波分解提取输入时间序列的时域信息和频域信息.随后,考虑双向信息流,采用双向长短时记忆网络(BiLSTM)进行预测,引入注意力机制,通过映射加权和学习参数矩阵赋予BiLSTM隐含状态不同的权重,有选择性地获取更多有效信息.最后,通过实际数据进行仿真验证.仿真结果表明,所提模型与现有模型相比,具有良好的预测性能.

W-BiLSTM Based Ultra-short-term Generation Power Prediction Method of Renewable Energy

The existing ultra-short-term prediction methods for renewable energy are difficult to effectively mine and analyze the inherent fluctuation rules of data. Important information is easy to be lost when the time sequence is too long. Therefore, a power prediction method of wind power and photovoltaic based on the wavelet bidirectional long-short term memory network (W-BiLSTM) with Attention mechanism is proposed. Firstly, wavelet decomposition is used to extract the time domain information and frequency domain information of the input time series. Then, considering the bi-directional information flow, the bidirectional long-short term memory (BiLSTM) network is used for prediction. Attention mechanism is introduced, and different weights are given to the hidden state of BiLSTM through mapping weighting and learning parameter matrix, so as to selectively obtain the more effective information. Finally, the experiment is carried out using actual data, and the results show that the proposed model has better prediction performance compared with the existing models.