Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load predictions, fine-grained load profiles can expose users' electricity consumption behaviors, which raises privacy and security concerns. One solution to improve data privacy is federated learning, where models are trained locally on private data, and only the trained model parameters are merged and updated on a global server. Therefore, this paper presents a novel transformer-based deep learning approach with federated learning for short-term electricity load prediction. To evaluate our results, we benchmark our federated learning architecture against central and local learning and compare the performance of our model to long short-term memory models and convolutional neural networks. Our simulations are based on a dataset from a German university campus and show that transformer-based forecasting is a promising alternative to state-of-the-art models within federated learning.

翻译：电力负荷预测是智能电网中协助供需平衡的关键任务。虽然先进的深度学习模型需要大量高分辨率数据以实现精确的短期负荷预测，但细粒度负荷曲线可能暴露用户的用电行为，从而引发隐私和安全问题。提升数据隐私的一种解决方案是联邦学习，即模型在本地私有数据上训练，仅将训练后的模型参数在全局服务器上合并更新。为此，本文提出了一种结合联邦学习的创新Transformer深度学习方法，用于短期电力负荷预测。为评估结果，我们将联邦学习架构与集中式学习和本地学习进行基准对比，并将模型性能与长短期记忆网络及卷积神经网络进行比较。基于德国大学校园数据集的仿真实验表明，在联邦学习框架下，Transformer预测模型是现有最优方法的一种极具前景的替代方案。