GREEN.DAT.AI
Energy-efficient
AI-ready Data Spaces
Channelling the potential of AI towards the goals of the European Green Deal
Vision.
To develop novel Energy-Efficient Large-Scale data analytics services, ready-to-use in industrial AI-based systems, that will reduce the environmental impact of data management processes.
We have the ambition to channel the potential of AI towards the Europe’s sustainability goals by implementing an AI-ready data space. An AI-ready data space is a data management framework designed to support the use of AI techniques.
The challenge.
The energy demand of AI systems is a growing concern, especially with the increasing use of deep learning and other computationally intensive algorithms. In addition to the training of models, AI systems also face high energy demands related to tasks such as inference, data storage and retrieval, and data centres’ cooling.
Training of models
Inference
Data Retrieval
Approach & solutions.
To address this challenge, GREEN.DAT.AI focuses on the development of novel Large-Scale Data Analytics Services that are designed to consume less energy. The AI Services Toolbox will include:
- AI-enabled data enrichment
- Incentive mechanisms for Data Sharing
- Synthetic data generation
- Large-scale learning at the edge/fog
- Federated & Auto ML at the edge/fog
- Explainable AI
- Federated & Automatic Transfer Learning
- Adaptive FL for Digital Twin applications
- Automated IoT event-based change detection and forecasting.
The project will also develop a benchmarking and evaluation framework for measuring and comparing the energy efficiency of different AI services, addressing the need for more accurate energy consumption models and the development of energy-aware AI algorithms.
Industrial Pilots
Novel AI services tested in real-life industrial settings.
GREEN.DAT.AI will demonstrate the efficiencies of the new large-scale data analytics services in four industries (Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking) and six different application scenarios, leveraging the use of European Data Spaces.
Energy
Data sharing across the renewable
energy sector
Agrifood
Water Management
Electromobility
Smart electric vehicle charging
Shared mobility
Energy demand response in e-bikes & infrastructure planning
Agriculture
Smart farming optimisation through Digital Twins
Banking
Fraud detection and explainable AI with privacy preservation
Our role.
Beyond project coordinator, Inlecom Innovation drives the Data Foundation and Reference Architecture task, and the commercialisation activities.
This project has received funding from the Horizon Europe research and innovation programme under GA 101037075. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the granting authority can be held responsible for them.
