The EU-funded Di-Hydro project is testing how AI, digital twins, forecasting and sensor networks can help hydropower operators make better operational decisions. Its central tool, the Di-Hydro Decision Making Platform, brings together inflow forecasts, energy-market signals, technical constraints and operational data to support optimized production schedules and scenario analysis.
Di-Hydro, short for Digital Maintenance for Sustainable and Flexible Operation of Hydropower Plant, is a Horizon Europe research project with approximately €4.5 million in EU support. It began on October 1, 2023 and is scheduled to run until September 30, 2026. Rather than presenting AI as a stand-alone feature, the project is building an integrated approach to hydropower operations that includes equipment condition monitoring, environmental sensing, data exchange and real-time visualization.
The project matters because hydropower operations involve competing considerations. Operators need to respond to available water, equipment condition, operational limits and energy-market conditions while also considering environmental factors. Di-Hydro is designed to give teams a more connected view of those inputs, then use AI-enabled forecasting and optimization to support their decisions.
According to the project's official overview of the Di-Hydro Decision Making Platform, the platform has been piloted across three use cases in Greece, Italy and Serbia. The pilots involve PPC in Greece, A2A in Italy and EPS in Serbia.
From fragmented operational data to decision support
At the center of the project is the Decision Making Platform (DMP). The DMP is a software environment intended to consolidate data that might otherwise sit in separate operational, forecasting and planning processes. It uses AI and forecasting capabilities to generate production schedules while enabling operators to test different scenarios and receive real-time decision support.
That approach is significant because a digital twin is more than a visual representation of an asset. In Di-Hydro, digital-twin concepts connect physical plant conditions, operational information and analytical models. The goal is to help teams simulate potential operating scenarios before acting, rather than relying only on isolated data points or manual coordination.
The project's documented components include:
- Forecasting and optimization based on inflow forecasts, market information and plant constraints.
- Digital twins and simulation for evaluating operational scenarios across sites.
- Predictive maintenance inputs informed by structural health and condition-monitoring sensors.
- Environmental and biodiversity monitoring, including real-time water-quality sensing.
- A secure, interoperable data architecture for sharing information across the project environment.
- Visualization tools designed to make operational and analytical information easier to interpret.
The platform has already been piloted, but Di-Hydro remains an ongoing research and demonstration effort. Its work is being refined alongside wider digital-twin capabilities, sensor development and deployment guidance. It should not be interpreted as a claim that every hydropower facility can immediately adopt a finished, identical system.
| Di-Hydro component | Data or capability involved | Operational purpose |
|---|---|---|
| Decision Making Platform | Operational data, inflow forecasts, market signals and technical constraints | Support optimized production schedules, scenario analysis and real-time decisions |
| Digital twins | Multi-site operational concepts and simulation | Evaluate operating scenarios before implementation |
| Condition-monitoring sensors | Structural health and equipment-condition data | Inform maintenance-related decisions |
| Environmental monitoring | Real-time water-quality and biodiversity measurements | Bring environmental factors into operational visibility |
What the Di-Hydro model means for energy operators
The immediate lesson from Di-Hydro is not that AI replaces operational expertise. It is that forecasting, sensors and simulation can make that expertise more effective when data is connected in a usable workflow. An operator can only act on an optimization recommendation if the underlying inputs, assumptions and technical limits are visible and credible.
For businesses that operate energy assets or provide services to utilities, the practical opportunity is to focus on a defined decision process. A useful starting point might be connecting maintenance signals to planning, combining environmental measurements with operational dashboards, or making forecast data available in one workflow. The value comes from improving a real decision that is currently slow, manual or based on incomplete information.
Potential returns can therefore arise through better production planning, earlier visibility into equipment condition and reduced effort spent assembling data for operational reviews. However, Di-Hydro does not provide a universal return-on-investment figure. The financial case will depend on a site's existing instrumentation, data quality, integration work, operational priorities and the decisions the system is expected to improve.
The project also highlights common adoption barriers. A digital twin depends on dependable source data, and sensor deployments require maintenance and validation. Forecasts and optimization outputs need to fit actual plant constraints. Interoperable data exchange and cybersecurity are part of the technical foundation, not optional additions after a pilot has been built.
Di-Hydro's Innovation Handbook reflects this broader implementation view. It organizes deployment lessons around environmental and biodiversity monitoring, digital twins and simulation, predictive maintenance, forecasting and optimization, cybersecurity and data exchange, and advanced visualization or augmented reality. That framing recognizes that operational AI is a system of connected capabilities, not simply a model added to a dashboard.
For companies considering similar projects, a phased approach is likely more practical than attempting to digitize every process at once:
- Identify one operational decision with measurable inputs and a clear owner.
- Assess the available sensor, maintenance, forecasting and operational data.
- Connect the most relevant systems into a reliable workflow.
- Test recommendations against real technical constraints and operator knowledge.
- Expand only after the workflow proves useful in day-to-day operations.
For energy operators, the Di-Hydro model shows why AI projects should start with operational workflows rather than a generic ambition to "use AI." Scalevise helps teams turn disconnected data, manual reviews and recurring decisions into practical automated processes through AI workflow automation services. The right implementation can reduce manual coordination while keeping people in control of important operating decisions. Discuss an AI automation project with Scalevise.
Frequently Asked Questions
What is the Di-Hydro project?
Di-Hydro is an EU-funded Horizon Europe project focused on digital maintenance and more sustainable, flexible hydropower operations. It combines AI, digital twins, sensors, forecasting and environmental monitoring.
What does the Di-Hydro Decision Making Platform do?
The platform ingests operational data, inflow forecasts, energy-market signals and technical constraints. It is designed to support optimized production schedules, scenario analysis and real-time decision-making.
Where is Di-Hydro being piloted?
The project has piloted its Decision Making Platform across use cases involving PPC in Greece, A2A in Italy and EPS in Serbia.
Can a digital twin improve hydropower maintenance?
A digital twin can help connect condition-monitoring data with operational models and scenario analysis. In Di-Hydro, structural health and condition sensors are being developed alongside predictive-maintenance and decision-support capabilities.
Conclusion
Di-Hydro is a practical example of how hydropower digitalization can combine AI with the data, sensors and operating context needed for useful decisions. Its multi-site pilots show that production planning, maintenance insight and environmental monitoring can be treated as connected operational challenges. The project's most durable lesson is that successful AI optimization depends on trustworthy data, clear workflows and technology that supports, rather than sidelines, operator expertise.
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