论文标题
深厚的强化学习辅助联盟学习，以实现强大的短期效用需求预测电力市场
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets
论文作者
论文摘要
短期负载预测（STLF）在电力贸易市场的运营中起着重要作用。考虑到对数据隐私的日益关注，在最近的研究中，越来越多地采用了联合学习（FL）来培训公用事业公司（UCS）的STLF模型。令人鼓舞的是，在批发市场中，由于发电厂（PPS）直接访问UCS数据并不现实，因此FL绝对是获得PPS准确的STLF模型的可行解决方案。但是，由于FL的分布性质和UC之间的激烈竞争，缺陷越来越多，导致STLF模型的性能差，表明仅采用FL是不够的。在本文中，我们提出了一种DRL辅助方法，缺陷感知的联合软性角色批评者（DearFSAC），以稳健地训练PPS准确的STLF模型，以预测精确的短期公用事业需求。首先。我们仅使用历史负载数据和时间数据来设计基于长期短期内存（LSTM）的STLF模型。此外，考虑到缺陷的不确定性，采用了深入的加固学习（DRL）算法来通过减轻缺陷引起的模型退化来协助FL。此外，为了更快的FL培训收敛，自动编码器设计用于缩小尺寸和上载模型的质量评估。在模拟中，我们验证了2019年赫尔辛基UCS的实际数据的方法。结果表明，无论是否发生缺陷，DearFSAC都胜过所有其他方法。
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.
