  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.
