精确的短期电力负荷预测对新型电力系统日发电计划的制订和实时调度至关重要，为取得准确可靠的负荷预测结果，针对真实用电负荷数据的时序性、不确定性等特征，提出了一种基于Attention-GAT-LSTM的智能算法，并应用在实际的新型电力系统中。在原始数据的处理中创新地结合了自注意力机制，引入了数据处理单元附加权值，并采用跳跃连接机制防止结果出现过拟合；将处理后的数据传递到图注意力网络（graph attention network，GAT）进行空间节点的特征提取，再传递到长短期记忆网络（long short-term memory，LSTM）进行时间特征的提取；通过前向传播、反向传播和梯度下降方法，使LSTM层的权重和偏置得到迭代更新，有效地减少信息在迭代过程中的丢失并突出关键时间点信息。最后通过多种不同模型的对比分析，验证了该方法在短期电力负荷预测（小时级）时具有较高的预测精度，可以为新型电力系统的运行调度、规划建设提供数据支持。

Accurate short-term power load forecasting is essential for the formulation of daily power generation plans and the real-time dispatching of new power systems. In order to obtain accurate and reliable load forecasting results, this paper proposes an Attention-GAT-LSTM intelligent algorithm targeting the temporal and uncertain characteristics of real power load data, and applies it in practical new power systems. In raw data processing, this algorithm innovatively integrates a self-attention mechanism, introduces a data processing unit to assign weights, and employs a skip-connection mechanism to prevent overfitting. The processed data is transmitted to a graph attention network (GAT) for spatial node feature extraction, then passed to a long short-term memory (LSTM) network for temporal feature extraction. Through forward propagation, back propagation, and gradient descent methods, the weights and biases of the LSTM layer are iteratively updated, effectively minimizing information loss during iterations and highlighting key time-point information. Comparative analyses with various models demonstrate the method’s high prediction accuracy in short-term (hour-level) power load forecasting, providing data support for the operational dispatching, planning and construction of new power systems.