Abstract: Smart grid has rich power infrastructure in industry 5.0 ,and there are many kinds and widely distributed load detection devices in smart grid, which leads to strong heterogeneity of user load data collected by edge load detection devices. Using distributed federated learning for load training of larger models is prone to unstable model convergence. To address this issue, an efficient training method for the partitioned federated learning model for the smart grid was proposed. This method applies the training of neural network models to the area from substations to users. By using the split layer, the global model for power load prediction is divided into the top model and the bottom model. The server first collects the resource information of the load detection devices, then uses the freshness index of the load prediction model to define the priority to select the training set of the load detection devices, and allocates appropriate batches for heterogeneous load detection devices for the training of the bottom model. The server merges the heterogeneous load detection device features in the training set to generate a larger mixed feature sequence, reducing the impact of device heterogeneity on the training data and improving the model accuracy. The KL-divergence is used to measure the distribution difference of the training set, and the batch size is fine-tuned to reduce the distribution difference. Based on the public power load curve dataset, three baseline methods were compared. In non-independent and identically distributed data, the accuracy of this method was up to 3.6%, 11.7%, and 12.9% higher than the baseline methods.

摘要： 工业5.0环境下智能电网拥有丰富的电力基础设施，智能电网负载检测设备种类繁多且分布广泛，使得边缘负载检测设备收集到的用户负载数据具有很强的异构性，使用分布式联邦学习进行较大模型的负载训练容易出现模型收敛不稳定的问题。针对该问题，提出了面向智能电网的分割联邦学习模型高效训练方法，该方法将神经网络模型训练应用在变电站到用户区域，通过分割层把电力负载预测这类全局模型分为顶层模型和底层模型。服务器先收集负载检测设备资源信息，再使用负载预测模型新鲜度指标定义的优先级来选择负载检测设备训练集合，并为异构负载检测设备分配合适的批量以进行底层模型训练。服务器通过合并训练集中的异构负载检测设备特征，得到较大的混合特征序列，减小设备异构性对训练数据的影响，提高模型准确性。使用KL散度来衡量训练集分布差异，通过微调批量大小减小分布差异。基于公开电力负载曲线图数据集，对比了三种基线方法，在非独立同分布数据下该方法的精确度比基线方法最高提高了3.6%、11.7%和12.9%。