CARTIF Projects


Intelligent Asset Management Platform for Hydropower operation and maintenance


iAMP-Hydro aims at improving the operation of existing hydropower plants (HPP) through the development of digital solutions which will be integrated into an intelligent Asset Management Platform (iAMP), encompassing secure open and transparent data-sharing protocols and three novel digital solutions. These include condition monitoring and predictive maintenance for turbines; ecological status monitoring for sustainable water resources management; and improved flow and power forecasting. The full package of solutions will be validated at five HPP in Europe, covering different power capacities, turbine types, water uses, climatic conditions and environmental sensitivities.



  • Co-develop and validate novel condition monitoring and predictive maintenance digital solution at TRL5 by the end of the project, for hydromechanical and electrical equipment.
  • Co-develop and validate advanced sensors and models for the monitoring of biodiversity parameters for an ecologically optimised hydropower operation to improve biodiversity, environmental and socio-economic sustainability of existing plants.
  • Co-develop and validate enhanced weather and flow forecasting models using operational data from power plants and open source data in differing climates, with differing power capacities and flow regimes achieving >23% improvement in reservoir inflow, outflow and water balance prediction accuracy.
  • Co-develop secure data collection, communication, storage and sharing protocols and standards to enable trusted data-driven operation and maintenance, and interoperability between hydro operators, and with other renewable energy sources.
  • Co-develop and validate iAMP decision-making algorithms for data-driven O&M, including integration of operations with other renewable energy sources (RES) to increase flexibility and optimise hydro positioning in energy markets.


  • CARTIF is leading the WP6: flow and available power predicition model development.
    Within the project, it will be developed an hybrid forecasting approach based on ensemble machine learning and existing physical models. Different AI-based techniques such as Long Short-Term Memory (LSTM) networks or other recurrent neural networks will be explored to define a common forecasting framework to be applied to the variety of regions and the validation sites of the Project

Expected Results

  • AI-based model for flow and energy forecast, validated in Reservoirs Hydropower plants. 
  • AI-based model for flow and energy forecast, validates in Run of River Hydropower plants. 

R&D Line

  • Research on advanced and intelligent strategies for the management, operation and maintenance of buildings based on AI/ML/DL for the generation of decision support systems.


Horizon Europe


Total Budget: 4,151,083.74€

CARTIF Budget: 371,250€

CARTIF Funding:371,250€

Duration: 01/10/2023 – 30/09/2026


PhD. Raquel López Fernández

Energy Division


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