融合注意力机制的CNN-GRU-LSTM电力系统短期负荷混合预测模型

A Hybrid CNN-GRU-LSTM Power System Short-Term Load Forecasting Model Incorporating Attention Mechanism

在电力系统运行过程中,准确预测短期电力负荷是确保电力系统安全经济运行的重要条件。传统单一负荷模型无法完全捕捉复杂系统的变化和非线性关系,预测精度较低。为此,提出一种融合注意力机制的卷积神经网络、门控循环单元和长短期记忆网络的CNN-GRU-LSTM-attention混合预测模型。利用CNN对多维数据特征进行提取,引入注意机制增强GRU和LSTM在序列数据处理中对长期依赖关系的建模能力,进一步提高模型预测精度。结合实际算例进行对比分析实验,结果表明:CNN-GRU-LSTM-attention混合预测模型较LSTM、CNN-LSTM和CNN-GRU-LSTM等模型的预测精度均有较大提升,验证了其有效性和优越性。

In the process of power system operation,accurate prediction of short-term power load is an important condition to ensure the safe and economic operation of power system.The traditional single load model cannot fully capture the changes and nonlinear relationships of complex systems,and the prediction accuracy is low.For this reason,a hybrid prediction model integrating convolutional neural network with attention mechanism,gated recurrent unit and long and short-term memory network is proposed.Using the extraction of multi-dimensional data features,the introduction of the attention mechanism enhances and the ability to model long-term dependencies in serial data processing,further improving the model prediction accuracy.Comparative analysis experiments are conducted with practical examples,and the results show that the prediction accuracy of the hybrid prediction model is greatly improved compared with that of,and and etc.,verifying its effectiveness and superiority.