AI for enhanced network assets predictive maintenance, integrating off-grid data with condition-based monitoring
AI services to be used to foster predictive maintenance strategies for system operators.
I-NERGY is an EU-funded H2020 innovation project around Artificial Intelligence (AI) for Next Generation Energy aiming at reshaping the energy sector value chain towards better business and operational performance, increased environmental sustainability, and the creation of a stronger social fabric propagating high social value among citizens.
I-NERGY will run for 3 years has an overall budget of approximately €5m and will distribute around €2m among its selected open call beneficiaries.
The project will launch 2 open calls to select 10 and 15 Bottom-up Projects. The 1st Open Call was targeted to SMEs and Startups developing building blocks for new AI algorithms/services and small-scale experiments with an expected outcome of fully functional prototypes. The 2nd Open Call was targeted to consortiums made up of Start-up / SME (service developer / provider), plus 1 EPES stakeholder to develop MVPs.
In both cases, the selection process prioritised projects maximising the impact of the platform and demonstrating the benefit of AI in products, processes, or services.
Following a careful review and assessment of 80 applications from 26 European Union and Associated Countries member states, the I-NERGY initiative selected the 15 winning proposals who have embarked on a nine-month-long Technical Transfer Programme (TTP) in March 2023.
Each beneficiary will receive a financial grant of up to 100,000 Euros and invaluable mentorship services.
The selected beneficiaries have the goal to develop new services on top of existing technologies (Minimum Viable Products) addressing specific cross-sectorial challenges within the Energy sector or an energy-related domain. The services are being developed and tested within a pilot setting in order to get to a fully functional stage with produced assets being published on Europe’s AI on-demand platform.
All selected proposals were submitted by consortia of 2 members (mandatory), made up of a technology service provider/developer (SME) and an infrastructure provider/data owner willing to implement an energetic solution (any entity) with the selected third parties representing a total of 12 European Countries.
For further details, access the comprehensive Evaluation Report here.
Additional information on the I-NERGY Open Call is available here.
Meet the winners here.
I-NERGY 1st Open Call
After the assessment and evaluation of 126 submitted applications from 27 European Union and Associated Countries, the I-NERGY project has selected the 10 winning proposals to join its first 6-month long Technical Transfer Programme, starting at the end of April 2022, which includes up to 50,000 Euros funding per beneficiary and mentoring services.
Meet the winners here.
AI services to be used to foster predictive maintenance strategies for system operators.
VEOLIA in this pilot, at building level, is covering the role of facility manager (FM), aiming at generating analytics to improve FM processes.
The aim is to support a condition-based and risk-based maintenance of both existing and innovative power components, as well as for the new digital environment.
Local power network congestion management is highly required due to high shares of intermittent RESs in the ASM headquarters area, as well as the need for reducing the reverse power flow power flow.
A prediction engine that optimizes operational planning and reserve allocation costs and overall improving reliability of the grid operations.
Demonstrating how AI analytics can contribute to achieving an economic benefit optimisation as well as the reduction of energy consumption in a DHN.
Addressing an Energy Saving Verification Service (IPMVP based) for increasing the trust on EP Contracts.
Improvement of the asset management and the operational efficiency of the local smart distribution grid; deployment of cross-network coordinated management and operation with water networks.
Improvement in operational efficiency of PV assets through the combined effect of optimised maintenance and increased assets efficiency. Increased self-consumption from local RES and electricity cost reduction.
Better prediction of impeding changes in retail client load distribution due to the increasing numbers of EVs in Greece. Prediction of availability of publicly available charging stations.
Investigating opportunities for prosumers as providers of balancing and ancillary services in the energy market.
Conceptualising the non-energy services for personal safety/security and AAL based on the deployed sensor infrastructure and aggregated data from elderly care house.
Reducing the uncertainty linked to energy efficiency investments, which can be attributed to the lack of relevant skills and ability to assess investments.
Contributing to EPC being more reliable, user-friendly, cost-effective, better quality, and EU legislation-compliant.
Enabling the local agency to plan the deployment of renewable sources in an effective manner, considering the future changing conditions derived from climate change.
A hands-on Case Study targeting the Energy module of SnowPower, developed by Amigo srl within the I-NERGY 1st Open Call.
In the SuperPower project (I-NERGY Open Call I), FuVeX has developed AI algorithms to automatically enhance the quality of power lines visual images. The use of this technology has been validated with real operations for an early adopter.
ADIOS, a project around anomaly detection for grid operational stability coordinated by IKIM ltd in Ireland, it will solve challenges in two areas of experimentation: AI applications in energy and Predictive maintenance.
SuperPower, a project around super-resolution applied to drone imagery to improve power line monitoring, led by FuVeX Civil SL in Spain, it aims to solve challenges in two areas of experimentation: AI applications in energy and Predictive maintenance.
SnowPower, a software as a service (SaaS) that enables utilities to monitor and predict hydropower generation, providing an estimate of the snow water equivalent, led by Amigo s.r.l. in Italy, it will be tackling challenges in two areas AI applications in...
SmartRIVER, a global AI-based Digital Twin solution for AI-driven hydropower energy intelligence and optimal production forecasting under the coordination of GECOsistema srl in Italy, it will be providing solutions for AI applications in energy.
Maintenet, a project bringing the power of prediction into the electric distribution network and critical assets, led by Mipu Energy Data in Italy, it will provide solutions in the predictive maintenance area of experimentation.
E-ModelOps, the world's first ModelOps platform tailored for energy open-source forecasting, optimisation and simulation use-cases led by Snowball Technologies AB (previously Greenlytics AB) in Sweden, it will solve challenges in different domains: AI app...
AI4Hydro, led by AvailabilityPlus GmbH in Germany and with the mission to extend the remaining useful life of hydro-turbines, it will deal with challenges in three areas of experimentation: AI applications in energy, Analytical applications in energy and ...
AI4Demand, an AI-based multi-layer tool for building energy consumption and demand prediction using local weather forecasts and sensor data, coordinated by AMPER S&C IoT S.L in Spain, will solve challenges in two areas of experimentation: AI applications ...
AI4Helios+, a project around the optimization of public lighting systems through AI, coordinated by Connecthink in Spain, is focused on ensuring that public lighting operators can adjust the degree of lighting to a minimum depending on the demand forecast...
LexaTexer provides an Enterprise AI platform to support the energy value chain with prebuilt, configurable AI applications addressing CAPEX-intense hydro assets like Francis and Pelton turbines and pumps.
Our long-term goal is to develop an integrated drone + AI system for electricity grid diagnostics, which will contribute to reducing economic losses, inspection costs, and environmental impact by providing a reliable AI-based tool for timely automatic def...
The goal of the SuperPower v2.0 is to develop the first fully automated power line inspection value chain powered by superresolution.
The main goal of this project is to make accurate forecasts on the network’s future behaviour, taking into account, among other topics, problems related to the increasing integration of Distributed Energy Resources in LV networks.
"Rebase.energy" is a digital energy startup based in Stockholm, Sweden, closely connected to the KTH (Royal Institute of Technology) ecosystem. Our vision is to accelerate the energy transition by empowering energy innovators with data and digital tools.
Environmental comfort takes a central role in the well-being and health of people. In modern industrial, commercial, and residential buildings, passive energy sources (such as solar irradiance and heat exchangers) and Heating, Ventilation and Air Conditio...
THRUST-AID is an AI-powered automatic powerline defect detection tool, trained on extensive ultra-high resolution aerial imagery data selected from the entire Lithuania’s transmission network. The developed AI analytics software is able to identify high-r...
To provide accurate measures of Cross Zonal Capacity, a key parameter is Net Position (the difference between generation and load of a zone). Here we describe our supervised model to predict net position for the next 48h that achieve an MAE of 75.8 and a...
After obtaining new meteorological data, we try to improve the results of session 1. The objective is to build a model to forecast Montenegro Cross Zonal Capacity 48h in advance. In this session, we achieved a model with a R2 of 0.7167 and a MAE of 67.0...