Optimizing steel structures for solar panels: integrating artificial intelligence and web-based Decision support systems for enhanced efficiency and sustainability

The optimization of steel structural systems for solar panel (SP) installations is crucial for improving energy efficiency and reducing costs in renewable energy systems. This study focuses on optimizing the efficiency of steel structural systems for SP using Artificial Intelligence and web-based applications. The study integrates Artificial Neural Networks (ANNs) with Finite Element Model simulations utilizing STAAD Pro V8i SS6 software and MathWorks® MATLAB® software to create effective SP support structures. The ANN model, trained on numerical analysis data from 29 sub-systems, can predict optimal design configurations with an accuracy of 97.9%. Also, the authors created A web-based decision support system (DSS) that allows users to input design criteria and retrieve optimized solutions, allowing users to input design criteria and retrieve optimized structural solutions based on location, cost, and energy output. The study identifies one-column, two-column, and four-column structural systems, comprehensively comparing energy production and structural weight. Results indicate that System C (four-column) is the most efficient in energy output, while System A (one-column) is more suited for smaller, low-cost installations. The ANN model demonstrates its ability to improve decision-making in structural design, providing practical applications for both residential and commercial installations. The findings indicate that using this web interface can significantly enhance energy output and reduce costs due to optimum weight structures in solar infrastructure. This study highlights the significant impact that ANNs can have on improving renewable energy systems by enhancing the efficiency and sustainability of future structural design advances.