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.
ICT-49 Project I-NERGY launched its first Open Call on 8 Nov, seeking SMEs and start-ups to develop building blocks and applications for new AI algorithms/services and small-scale experiments (prototypes) to address the developments and implementation of ...
I-NERGY, an EU funded innovation project around Artificial Intelligence for Next Generation Energy has launched an online community to foster networking, mentorship and ideas exchange for the AI and Energy ecosystem.
The final AI4EU event showcased outcomes from the project and brought together AI experts and SMEs to explore the next steps of the European AI on demand strategy.
In the frame of the European Union's participation at Expo Dubai, we invite you to join us on 15 and 16 March 2022, in a series of events aimed at presenting the "European AI Excellence and Trust in the world".
Learn about Open Calls for SMEs offering funding, support programmes and more.
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.
The next phase in the development of the AIOD, is the commencement of the €9m Coordination and Support Action project AI4Europe, which is tasked with further advancing the technical development of the platform and developing a supporting community to crea...
Find out how AI-solutions can be applied across different sectors and what support is offered for implementation of such AI solutions.
Find out how AI-solutions can be applied across different sectors and what support is offered for implementation of such AI solutions. Watch the recording.
I-NERGY Newsletter, Issue July 2022
This café is organised by the ICT-49 projects to encourage discussions on the opportunities and challenges associated with the adoption of AI, as well as how to best support and connect with different European regions, organisations and industry.
This café is organised by the ICT-49 projects to encourage discussions on the opportunities and challenges associated with the adoption of AI, as well as how to best support and connect with different European regions, organisations and industry.
Aggregated load time series of the Portuguese TSO (Transmission System Operator) for 2018 and 2019 (15 minute resolution) accompanied by encoded (in a cyclical manner) time covariates. Useful for TSO demand forecasting experimentation.
An MLflow Docker deployment accompanied by a Postgresql database for storing logged metrics and a MinIO storage for storing artifacts.
A Kmeans clustering model for prosumer's electricity load categorization meant for demand response applications.
Α seasonal ARIMA load forecasting model for boiler rooms in district heating networks (AIExperiments Asset).
Dataset contains radiation data measured by FAEN (Fundación Asturiana de la Energía) in a series of locations and during a period from 2008 to 2018.
A forecasting service for predicting the aggregated hourly net electrical load of the Portuguese transmission system operator (REN). The service makes use of an LSTM network.(AIExperiments Asset)
A databroker service used for loading timeseries to forecasting machine learning models
A forecasting service for predicting of the Portuguese aggregated electricity load time series (15-min resolution, 24hr forecasting horizon). The service makes use of the LightGBM algorithm. (AIExperiments asset)
A set of NiFi templates in XML format together with python scripts that serve as a starting point for creating your own DataFlow pipeline to collect data for ML/DL Models training and testing.
Forecast the Performance of Inverter Systems with multivariate inputs
The problem of finding faults in oscillating data in the form of 3-phase current and voltage values is considered. While these kind of data have high resolutions, we give a solution to multivariate changing point detection, framed as an anomaly detection...
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, started in April 2022...
Snow Water Equivalent (SWE) estimation at coarse resolution (Alpine watersheds of France, Switzerland, Italy and Austria).
The document describes the datasets and setup of the ML models used for the estimation of Snow Water Equivalent (SWE) and runoff in Alpine watersheds within the scope of SnowPower, one of the projects in the I-NERGY 1st Open Call. SnowPower is a Softwar...
Watershed-aggregated values of a SWE indicator for selected Alpine watersheds. Daily values from March 2017 to December 2020. Developed by AMIGO s.r.l. for the SnowPower project, part of the I-NERGY 1st Open Call.
