  Smart power grids are one of the most complex cyber-physical systems,
delivering electricity from power generation stations to consumers. It is
critically important to know exactly the current state of the system as well as
its state variation tendency; consequently, state estimation and state
forecasting are widely used in smart power grids. Given that state forecasting
predicts the system state ahead of time, it can enhance state estimation
because state estimation is highly sensitive to measurement corruption due to
the bad data or communication failures. In this paper, a hybrid deep
learningbased method is proposed for power system state forecasting. The
proposed method leverages Convolutional Neural Network (CNN) for predicting
voltage magnitudes and a Deep Recurrent Neural Network (RNN) for predicting
phase angels. The proposed CNN-RNN model is evaluated on the IEEE 118-bus
benchmark. The results demonstrate that the proposed CNNRNN model achieves
better results than the existing techniques in the literature by reducing the
normalized Root Mean Squared Error (RMSE) of predicted voltages by 10%. The
results also show a 65% and 35% decrease in the average and maximum absolute
error of voltage magnitude forecasting.
