Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not.

翻译：短期负荷预测（STLF）在电力交易市场的运行中扮演着重要角色。考虑到数据隐私问题的日益关注，近年来相关研究越来越多地采用联邦学习（FL）来为公用事业公司（UC）训练STLF模型。令人振奋的是，在批发市场中，由于发电厂（PP）无法直接获取UC的数据，FL无疑是PP获得精确STLF模型的可行解决方案。然而，由于FL的分布式特性以及UC之间的激烈竞争，缺陷日益增多并导致STLF模型性能下降，这表明单纯采用FL并不足够。本文提出了一种基于DRL辅助的联邦学习方法，即缺陷感知联邦软演员-评论家（DearFSAC），以稳健地训练出精确的STLF模型，帮助PP预测准确的短期电力需求。首先，我们利用仅包含历史负荷数据和时间数据的长短期记忆（LSTM）网络设计了一个STLF模型。进一步，考虑到缺陷发生的不确定性，我们采用深度强化学习（DRL）算法来辅助FL，以缓解因缺陷导致的模型退化。此外，为了加速FL训练的收敛速度，我们设计了一个自动编码器，用于上传模型的降维和质量评估。在模拟实验中，我们使用2019年赫尔辛基UC的真实数据验证了该方法。结果表明，无论缺陷是否发生，DearFSAC均优于其他所有方法。