基于CNN-LSTM及深度学习的风电场时空组合预测模型

为了更好地预测风电场的风电功率，提取风电场相邻站点之间时空信息和潜在联系，提出了一种基于卷积神经网络（CNN）、互信息（mutual information，MI）法、长短时记忆网络（LSTM）、注意力机制（AT）和粒子群优化（PSO）的短期风电场预测模型（MI-CNN-ALSTM-PSO）。CNN用于提取不同站点的空间特征，LSTM则用于获取多个站点的风电数据的时间依赖信息，据此设计CNN-LSTM时空预测模型，并结合深度学习算法，如MI特征选择、AT注意力机制、PSO参数优化，对模型进一步改进。通过两个海岛风电场的实验数据分析可知，所提模型具有最优的统计误差，CNN-LSTM模型可以高效提取风电场时空信息并进行时间序列预测，而结合深度学习算法（MI、AT和PSO）后的组合模型能进一步提高风电功率预测精度和稳定性。

Combined Spatiotemporal Wind Farm Prediction Model Based on CNN-LSTM and Deep Learning

In this study, a combined prediction model based on the mutual information (MI) method, convolutional neural network (CNN), long short-term memory (LSTM) network, attention mechanism (AT), and particle swarm optimization (PSO), is proposed to successfully predict wind farm power and extract the spatiotemporal information and potential connections between the adjacent sites of the wind farm. Here, CNN is used to extract the spatial features of different sites, and LSTM is employed to obtain the time-dependent information of the wind power data from multiple sites. Furthermore, a CNN-LSTM spatiotemporal prediction model is designed and used along with deep learning algorithms, such as MI, AT, and PSO, to further improve the proposed model. The analysis of the experimental data of the two island wind farms reveals that the proposed model achieves the lowest statistical error. The CNN-LSTM model can efficiently extract the spatiotemporal information of the wind farm and perform time series forecasting; moreover, the model combined with deep learning algorithms (MI, AT, and PSO) can further improve the accuracy and stability of wind power forecasting.