The proposed model employs complex pattern learning capabilities of recurrent neural networks and temporal pattern extraction of innovation state-space models. It is tested on GEFCom-14 and ISO-NE ...

Model-free deep reinforcement learning has emerged as a promising method for addressing the scheduling challenges in integrated energy systems. However, uncertainty in system states continues to ...