I-NERGY 2nd Open Call
I-NERGY has launched its last Open Call, apply before 12th December 2022!
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.
I-NERGY has launched its last Open Call, apply before 12th December 2022!
A hands-on Case Study targeting the Energy module of SnowPower, developed by Amigo srl within the I-NERGY 1st Open Call.
The AI4Demand project presents a short-term (daily or hourly) energy demand and consumption forecasting module using novel AI-based approaches where the cause-and-effect relations between energy data and externally influencing factors (energy and weather-...
The asset presents a datasets related to the hourly and daily electricity consumption of buildings located in Helsinki and weather-related indicators from the nearest meteorological station
The docker contains the scripts to apply the trained model, trained saved in the resource folder.
This container provide the services for label extension on a tagged dataset using a voting algorithm.
This asset is the open dataset used for the experiment and for develop the proof of concept. It is an open dataset of SCADA data for Energy Management Systems.
The docker contains the execution script to train the models on a dataset with extended labels
Demonstration web application for the AI4Hotels project within the I-nergy call. The demonstration webapp allows the user to visualize the data used for training the forecasting models, as well as run the electric load forecasting model and the occupancy ...
A repository with some of the models developed and trained for the AI4Hotels project.
Real electric load and occupancy data used to train the forecast models in the AI4Hotels project
Frontend and backend docker container for the application
A Gradient Boost model for building-level electricity load forecasting
A service for predicting river discharge in a specific river section for the next time step
The project SuperPower developed thanks to I-NERGY first open call has developed super-resolution algorithms to double the pixels in an image using Convolutional Neural Networks. These datasets consist of two subsets of original photos of medium-voltage p...
The project SuperPower developed thanks to I-NERGY first open call has developed super-resolution algorithms to double the pixels in an image using Convolutional Neural Networks. These datasets consist of two subsets of original photos of high-voltage pow...
Generating good quality data in power line inspection to enable the use of AI technologies to automate the power line inspection process using drones Power lines require regular inspections to guarantee their structural health. This process has been co...
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.
This notebook provides a simple workflow to perform discharge or energy forecast using ML .
In the project Super-power (I-NERGY Open Call I), FuVeX has developed Super-resolution algorithms to enhance the quality of the photos to inspect power lines. The execution of the algorithm is concentrated on the following line, which corresponds to t...
Link to a Git repository that includes the syntethic Data set and a Docker container that implements an API Gateway based on python Tornado framework, that implement the AI algorithm based on syntethic data, and the method useful to train and predict da...
This file contains a synthetic dataset to create baseline behavior models for transformers temperature. The dataset is a 5 columns containing the 4 temperature and the hour, with a 5 minutes frequency.
We are happy to announce that the 1st Technology Transfer Programme of I-NERGY - AI for Next generation Energy has been successfully completed. We invite you to join us at its Final Event taking place online on the 7th November 2022 at 11.00 CET where you...
We are happy to announce that the 1st Technology Transfer Programme of I-NERGY - AI for Next generation Energy has been successfully completed. We invite you to join us at its Final Event taking place online on the 7th November 2022 at 11.00 CET where you...
This is a LightGBM time-series forecasting model. LightGBM is a gradient boosting decision tree framework developed by Microsoft. It works by recursively partitioning the feature-space into hyperrectangles and utilising the mean (or median) of the target ...
This data broker can load open datasets from https://www.rebase.energy/datasets. This will enable access to all upcoming open datasets in the Rebase Platform. The goal of this broker is to make it easy for anyone on the AIOD platform to access additional ...
On Thursday, November 17th 2022, I-NERGY will hold the first webinar for the LAST Open Call.
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 ...
I-NERGY Transfer Learning Wizard is an application designed for novice data analysts to analyse energy data and develop machine learning (ML) models based on the Transfer Learning method.
The second and last open call for the I-NERGY Project has come to an end. The second open call ran from October 10 to December 12, 2022 and closed with 333 started applications and 80 submitted applications from 26 different countries! After weeks of diss...
Reducing errors in forecasting faulty PV performance by stacking deep neural networks
Charging sessions taking place in a single charger with maximum output of 11kW in Attiki Region, Greece
The service provides real time data related to electricity consumption centres and specifically EV charging stations via WebSocket connection protocol. The objective of the deployment is to help property owners to efficiently manage EV charging stations
This jupyter notebook is how a prediction system for cold energy demand was implemented in context of I-NERGY project.
This jupyter notebook is how a prediction system for heat energy demand was implemented in context of I-NERGY project.
This jupyter notebook is how a decision support system (DSS) was implemented in context of I-NERGY project.
This jupyter notebook is how a decision support system (DSS) was implemented in context of I-NERGY project.