基于Attention-GRU的短期电价预测

通过分析得出电价与负荷具有相关性，因此在电价预测模型中需要考虑实时负荷的影响。在此基础上针对前馈神经网络不能处理时序数据的缺陷与LSTM神经网络预测速度慢的问题，提出了一种基于Attention-GRU (Attention gated recurrent unit, Attention-GRU)的实时负荷条件下短期电价预测模型。该模型充分利用电价的时序特性，并采用Attention机制突出了对电价预测起关键性作用的输入特征。以美国PJM电力市场实时数据为例进行分析，通过与其他几种预测模型相比，验证了该方法具有更高的预测精度；与LSTM神经网络相比具有更快的预测速度。

Short-term electricity price forecasting based on Attention-GRU

Through analysis, it is concluded that there is a correlation between electricity price and load, so the influence of real-time load should be considered in an electricity price forecasting model. We consider the problem that a feedforward neural network can''t deal with time series data and the slow forecasting speed of the LSTM neural network. A real-time load forecasting model based on Attention-GRU is proposed. The model makes full use of the time series characteristics of electricity price, and uses an Attention mechanism to highlight the key input characteristics of electricity price forecasting. Taking the real-time data of PJM power market in the United States as an example, it is verified that this method has higher forecasting accuracy and faster forecasting speed than the LSTM neural network by comparing with other forecasting models. This work is supported by Natural Science Foundation of Shaanxi Province (No. 2017JM6105) and Basic Research Plan of Natural Science in Shaanxi Province (No. 2019JLM-11).