Digital Twin-Based Methodology for Predictive Monitoring of Photovoltaic Systems: Integration of IoT, BIM, and GIS

The growing intricacy in the administration of solar systems necessitates novel strategies to guarantee efficiency and reliability. This research presents a sophisticated methodology for developing a Digital Twin for the solar system at the University of Salerno campus. The primary aim is to create a dynamic digital model that facilitates real-time monitoring of the system's operational conditions and enhances predictive maintenance techniques. The integration of sophisticated technologies, including the Internet of Things (IoT), Building Information Modeling (BIM), and Geographic Information Systems (GIS), enabled the creation of a virtual replica of the physical system. The ThingsBoard platform centralizes and analyzes the real-time data collected by IoT sensors, while Autodesk Revit and Dynamo create a parametric BIM model that enables a comprehensive and interactive depiction of the system. Georeferencing and spatial analysis, executed using ArcGIS, enhance the Digital Twin, facilitating a thorough comprehension of the interactions between the system and its environmental surroundings. The findings indicate that the Digital Twin facilitates prompt anomaly identification, failure prevention, and the simulation of predictive maintenance scenarios. This approach enabled the optimization of operational management and a substantial reduction in maintenance expenses. The method shows how the Digital Twin can be used to effectively manage solar power plants and can serve as a model for managing other important infrastructures. This helps promote sustainable and resilient management.