The TWINVEST Project aims to develop a universal, open-source, and cybersecure Digital Twin to provide onshore wind farm investors with insights into operations and future investments. Spanning 42 months, the project unfolds in three stages: developing AI-based forecasting and monitoring models, integrating these into an interoperable Digital Twin, and validating the system with real wind farms and investment feasibility studies. TWINVEST’s technical work is organized into several work packages. It starts by gathering end-user requirements and defining both physical and virtual use cases. The architecture includes cybersecurity protocols and data modeling standards. A framework platform evaluates investment conditions like energy demand, pricing, grid capabilities, and storage needs. Another platform models turbine components and designs reliable wind farms using both bottom-up and top-down methods. Environmental modeling focuses on forecasting energy production using weather and spatial data, while a maintenance and risk platform optimizes OPEX through predictive maintenance and risk management. All components are integrated into a user-friendly, interoperable Digital Twin, validated through real-world and simulated use cases. Key achievements include a robust Digital Twin architecture, advanced modeling tools, and predictive methodologies for maintenance and investment planning. TWINVEST’s validated system demonstrates its value in enhancing operational efficiency and guiding investment decisions. A roadmap will ensure continued development beyond the project’s duration.