An Accurate GRU-Based Power Time-Series Prediction Approach With Selective State Updating and Stochastic Optimization

Accurate power time-series prediction is an important application for building new industrialized smart cities. The gated recurrent units (GRUs) models have been successfully employed to learn temporal information for power time-series prediction, demonstrating its effectiveness. However, from a statistical perspective, these existing models are geometrically ergodic with short-term memory that causes the learned temporal information to be quickly forgotten. Meanwhile, these existing approaches completely ignore the temporal dependencies between the gradient flow in the optimization algorithm, which greatly limits the prediction accuracy. To resolve these issues, we propose a novel GRU model coupling two new mechanisms of selective state updating and adaptive mixed gradient optimization (GRU-SSU-AMG) to improve the accuracy of prediction. Specifically, a tensor discriminator is used for adaptively determining whether hidden state information needs to be updated at each time step for learning the extremely fluctuating information in the proposed selective GRU (SGRU). In addition, an adaptive mixed gradient (AdaMG) optimization method that mixes the moment estimations is proposed to further improve the capability of learning the temporal dependencies information. The effectiveness of the GRU-SSU-AMG has been extensively evaluated on five different real-world datasets. The experimental results show that the GRU-SSU-AMG achieves significant accuracy improvement compared with the state-of-the-art approaches.