Sequence-to-Sequence Forecasting-aided State Estimation for Power
  Systems
Kamal Basulaiman Masoud Barati
Power system state forecasting has gained more attention in real-time
operations recently. Unique challenges to energy systems are emerging with the
massive deployment of renewable energy resources. As a result, power system
state forecasting are becoming more crucial for monitoring, operating and
securing modern power systems. This paper proposes an end-to-end deep learning
framework to accurately predict multi-step power system state estimations in
real-time. In our model, we employ a sequence-to-sequence framework to allow
for multi-step forecasting. Bidirectional gated recurrent units (BiGRUs) are
incorporated into the model to achieve high prediction accuracy. The dominant
performance of our model is validated using real dataset. Experimental results
show the superiority of our model in predictive power compared to existing
alternatives.