在加快构建新型电力系统背景下，提升负荷预测精度是保障电力系统经济、安全、稳定运行的重要措施，也是推动智能电网发展的关键所在。为增强对地区负荷的预测能力，提出一种气象影响因子动态自适应的短期负荷预测方法。首先，建立了基于并行多尺度时域卷积神经网络的负荷/气象信息融合模块，挖掘历史负荷与区域天气预报的多时间周期的变化模式；然后，提出了基于深度门控残差神经网络的气象因子动态辨识模块，通过动态调整特征贡献度并优化特征选择，增强对不同时空尺度特征权重的融合，提升模型对关键特征的提取能力；最后，以京津冀某区域的负荷数据进行算例分析，证明所提区域负荷预测方法具有更高的预测精度，对区域负荷的趋势性变化有较好的追踪效果。

In the context of accelerating the construction of a new power system, improving the accuracy of load forecasting is an important measure to ensure the economic, safe and stable operation of the power system, and it is also the key to promote the development of smart grid. In order to enhance the ability of regional load forecasting, a short-term load forecasting method based on dynamic adaptive meteorological impact factors is proposed. Firstly, a load/meteorological information fusion module based on parallel multi-scale temporal convolutional neural networks is employed to mine the multi-time period change model of historical load and regional weather forecast. Then, a dynamic identification module of meteorological factors based on depth-gated residual neural network is proposed. By dynamically adjusting the feature contribution and optimizing the feature selection, the fusion of feature weights of different spatio-temporal scales is enhanced, and the ability of the model to extract key features is improved. Finally, the load data of a region in Beijing, Tianjin and Hebei are used as an example to prove that the proposed regional load forecasting method has higher forecasting accuracy and better tracking effect on regional load trend changes.