A Novel Deep Reinforcement Approach for IIoT Microgrid Energy Management Systems

Introducing Deep Learning in the Industrial Internet of Things (IIoT) brings many benefits, such as network resilience and bandwidth usage reduction. In this work, we propose an innovative reinforcement learning architecture to implement distributed energy management systems for microgrids. The architecture is based on novel reinforcement learning and on time series prediction. The designed reinforcement learning uses classical recurrent neural networks instead of the habitual SAR (State Action Reward) method that most of the recent bibliography considers. We applied various techniques (Exact resolution, Rule-Based, Q-Learning, and our designed reinforcement learning) on a distributed IIoT energy control architecture. The proposed method has shown better results compared to the exact resolution and the Q-Learning algorithm. It results in fast learning systems with a small number of training samples. We identified and tested several management strategies. Integer Linear Programming (ILP) optimal expressions and strategy-based implementations are derived. We utilize the obtained results to train the recurrent neural network. Comparative results are very encouraging and prone to a generalization of our approach instead of the classical methods.