Optimised renewable energy

With solar photovoltaic capacity and other forms of renewable energy gaining traction in the EU, COSMIC will deploy predictive maintenance solutions to optimise renewable energy systems, cut maintenance costs and expand Renewable Energy Communities across Europe. 

Flood risk mapping for fair insuranceFighting energy poverty in social housingSizing an energy-storage system for on- shore power supplyEnergy sustainability indicators basedon waste separationLeak detection inbiomass district heatingEnergy optimisation anddecarbonisation of wastewatertreatment plantsUrban cooling system using rainwaterSustainable management of a renewable energy communityPredictive maintenance of PV plantsSustainableresidential buildingsTertiary-use buildings (schools)Industrial energy communitiesEfficient usage of heat pumpsRenewable energy communityPrivate housing in multi-family buildings

Pilots are real-world testbeds where Core technologies and AI innovations are combined to develop and validate scalable energy solutions.

PV plant in the filed

Image from pexels.com by Mark Stebnicki

Pilot location in France 

The pilot focuses on optimising photovoltaic (PV) plant performance by combining diverse data sources and AI-driven analytics to enhance equipment health monitoring, guide maintenance and replacement decisions, and maximise energy yield and operational efficiency.

Core technologies:

  1. Data Standardisation and Preparation
  2. Energy‑Grid Optimisation Microservices
  3. Intraverse

AI solutions from Third Parties: 

  • Monitoring-diagnostic imagery solutions 

  • Local solar irradiance models 

  • PV materials performance database 

Problem addressed: Performance losses by PV plants due to undetected equipment degradation and inefficient maintenance scheduling 

Expected outcomes: Improved predictive maintenance and performance optimisation, Enhanced operational decision-making through AI-driven tools

A solar panel of

Image from pexels.com by Robert So

Pilot location in Portugal 

This pilot focuses on optimising the operation and resilience of a renewable energy community by deploying AI-powered predictive maintenance and scenario analysis tools to ensure reliable asset performance and informed decision-making for sustainable energy management. 

AI solutions from Third Parties: 

  • The total cost including maintenance or interruption times, should be included in the assessment in a levelized cost of energy (LCOE) or total cost of ownership (TCO) approach 

  • Resilience/independence factor will be assessed by TPs 

Problem addressed: Complexity and inefficiencies of managing energy generation, consumption and asset maintenance within a renewable energy community 

Expected outcomes: Enhanced reliability and efficiency of community energy assets, Informed strategic planning for community energy management 

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