Community Energy Management System Based on Deep Learning and Lagrangian Programming Neural Network

In this paper, a innovative approach is proposed for solving the energy scheduling problem (ESP) in community energy management system (CEMS), combining deep learning with lagrangian programming neural network (LPNN). First, The LPNN is employed to address the ESP, and its effectiveness is thereafter validated. Subsequently, a deep learning-based LPNN (DLPNN) model accompanied by a specially designed loss function is introduced. This enables the trained DLPNN to solve the ESP in CEMS without the need for repeated steps, which is a limitation of using LPNN alone. Additionally, the dynamic variables in the ESP are incorporated as input variables for training the DLPNN, allowing the model to determine optimal solutions for varying values of these variables. Ultimately, the effectiveness of the introduced approach is confirmed through simulation experiments.