基于双注意力机制的Seq2Seq短期负荷预测

Seq2seq Short-Term Load Forecasting Based on Double Attention Mechanism

针对经典的深度学习方法在多步长预测精度不高问题,提出一种基于双注意力序列到序列的短期负荷预测模型。通过自注意力机制有效提取影响负荷数据的隐藏相关因素,使模型能更好地发现负荷数据之间的规律,自适应地学习了负荷数据之间的相关特征,时间注意力机制捕获与时间相关的时序特征。经2个实际负荷数据实验,仿真结果表明,在(t+12)预测情况下,模型评价指标MAPE(Mean Absolute Percentage Error)为2.09%,较LSTM(Long Short-Term Memory)模型损失下降56.69%。验证了模型的正确性和可行性,模型较线性回归、 LSTM模型和Seq2Seq(Sequence to Sequence)模型的预测效果更好。

Aiming at the problem that the classical deep learning method has low accuracy in multi-step load forecasting,a short-term load forecasting model based on double attention sequence to sequence is proposed.Through the self-attention mechanism,the hidden related factors affecting the load data are effectively extracted,so that the model can better find the laws between the load data,adaptively learn the related characteristics between the load data,and the temporal-attention mechanism captures the time-related time-series characteristics.Through two actual load data experiments,the simulation results show that under the condition of(t+12)prediction,the model evaluation index MAPE(Mean Absolute Percentage Error)is 2.09%,which is 56.69%lower than that of LSTM(Long Short-Term Memory)model.The validity and feasibility of the model are verified.The prediction effect of the model is better than that of linear regression,LSTM model and Seq2Seq(Sequence to Sequence)model.