Who will help implement the AI solution? 

GECOsistema is a specialist company providing advanced engineering cloud-web, data-science and modeling studies and services in the field of environmental, climate risk and geospatial intelligence. 
We combine advanced data science and machine learning, environmental modelling, GIS and Geospatial Analysis tools, remote sensing, predictive analytics, to give you the critical insight you need into environmental, climate and geospatial issues. 

What is the AI solution the project plans to implement? 

SmartRIVER is a Digital Twin of the plant catchment, relying on worldwide available forecasts, big open climate, geospatial and satellite data, and AI at the core for efficient energy forecasting, with major advantages over complex models: 

• No specialist hydrologist skills, no physical model to tune. 

• New upstream services (e.g., Copernicus Data Store forecasts) can be used immediately. 

• Lightweight cloud deployment, just a web browser is required to users. One forecast service ranging from single Hydropower plant to multiple assets everywhere, to optimize resources (reservoir management and energy production strategy) and to save money (energy trading). SmartRIVER value proposition is exploited as Software as a Service (SaaS), with on demand subscription, for technicians and for energy traders; to be activated in a few steps for any hydropower plant of interest, supporting water resources management and energy production. 

The I-ENERGY project will support GECOsistema in developing a new SmartRIVER showcase concept in one river station that serves hydropower generation. This will demonstrate potential of the service to go far beyond actual approaches, mainly based on traditional complex hydrological models with long Modeling chains, huge data requirements (soil type, topography, geology, snow depth…). 

Within I-NERGY we will move much further to a fully functional prototype at TRL 6, demonstrating a running and operational forecast service, relying on state of art backend AI components, in a relevant case study area, but with high replicability worldwide.