【摘要】 在电力系统的经济调度中，如何合理利用电力负荷的过去和现在来推测其未来价值，具有非常长远的社会经济价值.短期电力负荷数据具有明显的时间特征，传统的深度模型越来越多地应用于该领域.然而，深度模型可能存在梯度爆炸或梯度消失，为此，提出了一种注意力机制优化长短期记忆网络的短期负荷预测模型.该模型将长短期记忆网络单元中的激活函数改进为加权激活函数组，并加入注意力机制以提高预测精度.更多还原

【Abstract】 In the economic dispatch of power system,how to reasonably use the past and present of power load to speculate its future value has very long-term social-economic value. Short-term power load data has obvious temporal characteristics,and the traditional deep model is more and more applied in this field. An improved AM-LSTM short-term load forecasting model is presented. The model improves the activation function in LSTM unit into weighted activation function group,and adds attention mechanism to improve the prediction accuracy.更多还原