Accelerating Sustainable Energy Operations with AI-Ready Digital Twins
No-code operational management digital twin platforms improve geothermal system efficiency through real-time data, spatial integration, and predictive maintenance.
The sustainable energy digital twin testbed demonstrates the use of a no-code enterprise platform to create a full-featured digital twin for geothermal operations. It aims to validate that geothermal operations can be more efficiently managed using AI-ready digital twins integrated with spatiotemporal data and real-time monitoring.
The testbed showcases scalable geothermal digital twin systems for complex energy infrastructure, enabling rapid deployment, spatial integration, predictive maintenance, and event handling in the renewable energy and industrial operations domain.
The no-code operational management platform enhances geothermal management by enabling the creation of AI-ready digital twins integrated with spatiotemporal data and real-time monitoring. It allows users to simulate and configure geothermal components (e.g., wells, exchangers, turbines) and provides tools such as event triggers, data dashboards, GIS overlays, and visualization for simulation and monitoring. This approach facilitates rapid deployment, spatial integration, predictive maintenance, and event handling, making geothermal operations more efficient and scalable without requiring extensive coding expertise.
This testbed demonstrates the practical application of digital twins in renewable energy infrastructure. It highlights how AI and automation can optimize geothermal energy operations at scale. As clean energy becomes a global imperative, geothermal digital twins will play a pivotal role in driving sustainable energy innovation.
The sustainable energy digital twin testbed contributes to industry advancement in the following ways:
Standard Alignment: Promoting digital twin creation aligned with industry standards such as ISO, IEEE, and IEC, helping ensure consistency, interoperability, and trust in geothermal energy systems.
Best Practices: Demonstrating best practices in low-code/no-code deployment, enabling non-technical users to implement advanced digital twin technologies and accelerating innovation across energy operations.
Scalable Pattern: Providing a scalable model for geothermal and other clean energy sectors, enabling organizations to adopt AI-enhanced infrastructure management that grows with operational needs.
Predictive Maintenance: Enabling predictive maintenance and event handling through real-time monitoring and analytics, improving operational efficiency, reducing downtime, and extending asset life.
Clean Energy Adoption: Supporting the broader adoption of clean energy technologies by showcasing how digital twins can efficiently manage complex geothermal systems and increase return on infrastructure investment.
Member, Lead Developer
