I-NERGY 1st Open Call: Meet the winners!

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.

Teams have the goal to develop building blocks and applications for new AI algorithms/services and small-scale experiments (Prototypes) to address specific cross-sectorial Challenges within the Energy sector and to enhance Europe's AI on-demand platform.

The 10 finalists are made up of small and medium-sized enterprises from 6 European Countries.

Download the analytical Evaluation Report here.
More Information for the I-NERGY Open Call are available here.

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Short Description
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.

Who will help implement the AI solution?
IKIM Digital Solutions was founded in 2018 and incorporated in 2020 in Ireland, and is focused on the provision of digital transformation solutions for the energy sector. Over the past 5 years, the team has developed machine learning models and software components for research institutes and energy system operators across Europe and the US - for anomaly detection, building models and alarm visualisation.

The core team members have been working together since 2013 as software developers and technology solution providers in collaboration with successful software Start-ups and EPC companies.

What is the AI solution the project plans to implement?
Our ADIOS project aims to improve power grid operation stability and increase the integration of distributed renewable energies in the European power grid. The objective is to apply deep-learning techniques for early-stage anomaly detection on power generators and power grid components reducing contingencies and power line strains.

The three primary objectives of the project will lead to new applications of deep learning for anomaly detection using a combination of techniques such as LSTM, GAN and autoencoders on substation and high voltage line sensor datasets to reduce faults and power flow imbalances improving grid operational stability.

The knowledge and data used will be embedded in an open-source code repository for dissemination and communication of the advancements in state of the art and published on the AI4EU platform.

The project will have a relevant impact on the EU policy regarding a reliable provision of energy - reducing carbon emissions, pollution, and fossil fuel - toward a resilient and greener integrated smart grid.

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Short Description
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 in energy and Demand forecast.

Who will help implement the AI solution?
AMPER S&C IoT (S&C) is a technology SME founded in 2006 in Barcelona (Spain) developing innovative solutions for the smart energy, smart buildings, and industry 4.0 sectors. Innovation is at the core of its business strategy, and it actively participates in research and innovation projects to add new functionalities and services to its commercial solutions. Our two main commercial solutions include enControl - a complete smart home solution, and ioLocate - a real time asset tracking solution.

What is the AI solution the project plans to implement?
We are excited to be part of the I-NERGY community through our open call project AI4Demand in which we are developing a short-term energy demand and consumption forecasting module using novel AI computational approaches where the cause-and-effect relations between energy data and external influencing factors like weather forecasts are considered and analyzed. This can provide an in-depth analysis of rapid changes in consumers’ behavior and thus offer more flexibility in the management of energy systems. We hope to integrate the results from this project in our commercial solution in the future and contribute to the AIoD and I-NERGY ecosystems and distribute it under OSI-approved open-source license.

For further information about AI4Demand project, you can contact on spaces @diguaruchamy or you can send an email at: digu.aruchamy@sensingcontrol.com

For more information about our company and the innovative projects that we are involved in, please visit this website.

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Short Description
AI4EOHotel, bringing to life Big-data from Hotels to forecast their Energy Consumption Patterns and Optimise their Energy Use, led by Energy Research & Intelligence Solutions, S.L.U. (EnergyRIS) in Spain, it will solve challenges in different domains: AI applications in energy, Analytical applications in energy, Monitoring, energy usage optimisation and Demand forecast.

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Short Description
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 Predictive maintenance.

Who will help implement the AI solution?
LexaTexer provides an Enterprise AI platform to support the energy value chain with prebuilt, configurable AI applications addressing CAPEX intense hydro assets like Francis turbines and pumps.

What is the AI solution the project plans to implement?
Hydro power operators face a number of challenges due to the introduction of stochastic energy providers like wind and solar. They are switching from baseload to more flexible power production, which introduces stochastic usage pattern. At the same time availability and efficiency must be improved. Static wear models don’t suffice any longer, intelligent AI driven condition monitoring and diagnostics promise remedy.

In this project we propose to combine our AI platform and data from real world operations to model the remaining useful life (RUL) of Pelton turbines based on real-world operational and environmental data. Thus, increasing RUL, efficiency and availability significantly. We propose to build hybrid AI models, including operational knowledge into the models.

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Short Description
DemandData, an initiative Releasing the value of smart meter data led by Advanced Infrastructure OÜ in Estonia, will solve challenges in different areas of experimentation: Data governance and data valorisation for energy services, Analytical applications in energy, Monitoring, energy usage optimisation and Demand forecast.

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Short Description
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 applications in energy, Data governance and data valorisation for energy services, Analytical applications in energy, Monitoring, energy usage optimisation and Demand forecast.

Who will help implement the AI solution?
Rebase is a digital energy startup based in Stockholm and operating in Europe. Rebase empowers energy service providers and energy companies with data and AI-based tools to plan and optimise distributed energy resources including solar PV, batteries and electric vehicles.

Using RebaseModel, energy companies and real-estate owners can optimize and streamline the planning and operation of wind, solar PV, batteries, heat pumps and electric vehicles. Customer benefits include 20% energy forecast accuracy improvement, 90% time savings for optimised planning analyses.

If you are interested in our company and/or I-NERGY project, please get in touch! You can reach us as info@rebase.energy.

To learn more feel free to check out our website and follow us on LinkedIn.

What is the AI solution the project plans to implement?
In the I-NERGY project E-ModelOps, Rebase is developing the first every ModelOps platform tailored for modelling distributed energy resources. The platform allows to accelerate creation, iteration, backtesting and deployment of state-of-the-art machine learning and optimisation models. As the more and more distributed energy resources gets integrated into the power grid more and better energy modelling is needed - this is exactly what E-ModelOps is enabling!

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Short Description
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.

Who will help implement the AI solution?
Mipu Energy Data offers a comprehensive portfolio of services, technologies and trainings based on our competences related to Energy Efficiency, Predictive Maintenance and Reliability and on our know-how in Predictive Modelling, extensively employing Artificial Intelligence models to perform data analysis. In particular, Mipu is active in provision of consulting services and trainings for Energy Managers, consultants and corporates, helping to reach energy efficiency goals, to cut waste and save money. Our experienced team carries out energy audits to analyse consumption and elaborate energy models of prediction and control. In addition to that, Mipu provides consulting services, software and hardware solution to digitize industrial, manufacturing environments, facilities and infrastructures, where assets are augmented with wireless connectivity and sensors, connected to a system that can visualize the entire process, control, and make decisions on its own. In particular, predictive maintenance is a method of preventing asset failure by analysing operating and maintenance data to identify patterns and predict issues before they happen. The expertise developed in the industrial field has enabled us to move forward in the field of digital innovation integrating services that apply Machine Learning and Artificial Intelligence to industrial processes. The know-how related to the application field is fundamental for the development of solutions able to provide a comprehensive response to the needs of digitization and optimization of operations.

What is the AI solution the project plans to implement?
Leveraging on our mix of engineering and data science knowledge, we proposed the integration of MAINTENET solution in the AI on demand platform, obtaining I-NERGY Technology Transfer Programme funding. The project aims at developing Artificial Intelligence models to constantly monitor the health status of assets within the distribution network. Machine Learning is effective in modelling the behaviour of assets abiding underlying physical laws. In particular, it is possible to precisely model the relationship between assets input and output variables, so that when the observations significantly differ from the algorithm’s prediction, we have an indication of an anomaly bound to occur.

If you are curious to know more about Mipu, our products, services, references and successful projects, do not hesitate to visit us at https://mipu.eu/ or write to info@mipu.eu.

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Short Description
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.

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.

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Short Description
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 energy and Monitoring, energy usage optimisation.

Who will help implement the AI solution?
Amigo is an Italian SME specialized in providing climate services to large international organizations. Since its foundation in 2013, Amigo has been active in consultancy, and was also one of 6 teams winning the 1st European Data Incubator in 2019. The team includes highly motivated, multi-disciplinary experts with expertise ranging from climate science to Big Data and Artificial Intelligence, from strategic design to business development. Physicists and computer scientists are supported by business experts to develop services that address the specific customer needs.

What is the AI solution the project plans to implement?
Amigo aims to develop SnowPOWER, a Software as a Service for monitoring and forecasting hydropower generation for energy companies, focusing on estimating the amount of water expected to flow into the reservoir based on a snow water equivalent assessment with a medium-term forecast (1 to 6 months). The specific objective of the project is to first forecast the energy generated for the spring and summer seasons using seasonal forecast. The support from the I-NERGY team is crucial in two ways: business development and AI support. Amigo intends to obtain support to define the business model and to discuss and revise the AI/ML approach to validate the proposed technology and possibly modify the techniques.

Contact us at: snowpower@amigoclimate.com

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Short Description
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.

Who will help implement the AI solution?
FuVeX's vision is to enable the autonomous digitization of power lines by using long-range drones instead of crewed helicopters. The company is based in Spain, and is currently working with the corporations that own the 95% of the Spanish medium/high voltage grid. Its goal in the next 2 years is to scale up its operations into the whole EU as drone regulations progress.

What is the AI solution the project plans to implement?
The goal of inspecting power lines is to detect defects in the infrastructure to repair them as soon as possible. Consequently, to perform accurate maintenance of these infrastructures, power line owners need as high-resolution visual imagery as possible. While this is relatively easy in crewed helicopters capable of carrying heavy cameras, obtaining high-quality data with small drones is very challenging as they are not capable of carrying heavy payloads.

For further information check: www.fuvex.com and the Joint Press Release with Naturgy (3rd biggest Spanish utility). 
Reach Fuvex at: info@fuvex.com