Using Deep Learning for Energy Modeling in Intelligent Structures with Decentralized Power Generation

Abstract

In this research, we propose new methods to enhance the construction, designs, also hybrid renewable energy plant maintenance by incorporating battery energy storage systems. Our investigates focused by the three key aspects of renewable electricity energy monitoring: size optimization, improved combination forecasts of wind and solar energy production using deep reinforcement learning, and an advanced battery life forecasting model employing Hidden Markov Models and network analysis to ensure equilibrium throughout the battery’s lifespan, with the utilization of the ZSoft Fuzzy method. To address the renewable energy sources of dynamic nature connected with grid, we consider a load requirements significance and select a reliable operational mode accordingly. Additionally, we apply Z-Soft Ridge Regression (ZS-Fuzzy) analysis to investigate if matching the battery capacity with the system would enhance its service life. By resolving architectural challenges, we determine that an optimal mix of renewable energy sources, with the PV plant contributing 37.1% of the total, offers the best solution. Moreover, our findings indicate that a BESS capacity of 12.9% relative to the HREP capacity satisfies the design criteria effectively.