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.

Keywords:

• grid connected wind energy
• photovoltaic systems
• fuzzy approach
• grid RES assessment

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

No participation of humans takes place in this implementation process.

HUMAN AND ANIMAL RIGHTS

No violation of Human and Animal Rights is involved.

DATA AVAILABILITY STATEMENT

Not Applicable.

CONFLICT OF INTEREST

Conflict of Interest is not applicable in this work.

AUTHORS’ CONTRIBUTIONS

All authors are contributed equally to this work.

Additional information

Funding

The author extends his appreciation to the Deanship of Scientific Research at University of Tabuk, Saudi Arabia for funding this work through grant number 0218-1440-S.

Notes on contributors

Ahmed Alzahmi

Ahmed Alzahmi received his M.S. degree in computer engineering from the Florida Atlantic University (FAU), Boca Raton, Florida, in 2010 and Ph.D from Southern Methodist University, Dallas, Texas, in 2018. He is currently an Assistant Professor at Faculty of Engineering - University of Tabuk, Saudi Arabia. His current research area includes 3D/2D memory interface design, RF transceivers design, PDN, and energy harvesting circuit designs.