Green.DAT.AI: Europe's €16M Project to Make Industrial AI Energy-Efficient Launches

Artificial intelligence is changing the way industries work, but it comes with a hidden cost, high energy use. Running large AI systems takes massive computing power. This means more electricity, more heat, and more pressure on the environment. In fact, global data centers already use around 1-1.5% of the world’s electricity, and the number is rising as AI adoption grows.

Europe is stepping in with a bold response. The Green.DAT.AI project, backed by €16 million, aims to make industrial AI smarter and greener. Its goal is simple yet ambitious: cut energy use without cutting performance. 

The project brings together researchers, industries, and policymakers to design AI that is powerful but energy-efficient. It improves algorithms and changes data storage to save power at every step. This is more than a technology upgrade; it’s a shift toward AI that supports climate goals while boosting industrial innovation.

Background: The Need for Green AI

Artificial intelligence can be powerful. It can also be energy hungry. Deep learning, big data, and complex models demand a lot of power. That power often comes from burning fossil fuels. This increases carbon emissions and strains the environment. Training one big model, for instance, can release as many emissions as hundreds of flights. That is not small. At the same time, data centers burn energy for storage, cooling, and running algorithms. AI is also creating more e-waste by replacing hardware quickly. The world needs a smarter way.

Europe aims to tackle this problem. The Green Deal sets a climate-neutral goal for 2050. AI must follow the same path. The push is not just to use AI but to make it sustainable. GREEN.DAT.AI is part of that effort, blending innovation and green priorities.

Inside the GREEN.DAT.AI Project

GREEN.DAT.AI is a €16 million EU-funded effort under Horizon Europe. It started in January 2023 and runs until December 2025. The aim: build energy-efficient data analytics tools that fit into industrial AI systems. It will prove these tools in four key industries, Smart Energy, Smart Agriculture/Food, Smart Mobility, and Smart Banking, across six real-world situations.

The project plans to reuse proven tech from earlier Horizon 2020 schemes. The goal is to deliver an open-source, green AI-ready platform and a toolbox at high technology readiness (TRL 7/8). SMART data spaces, federated learning, and edge/fog deployments all play a role. The toolbox includes services such as AI-enabled data enrichment, synthetic data generation, federated and automatic transfer learning, and IoT-based forecasts.

A consortium of 17 organizations from 10 countries, including research centers, universities, banks, and tech firms, drives the work.

How It Works: Innovations & Strategies?

The design is smart and practical. GREEN.DAT.AI shifts processing from central cloud servers to edge and fog devices. That cuts data movement. It speeds up responses. And it saves power.
Federated learning ties all this together. Data stays local. Models learn across devices without exposing private data. Other methods, like automated transfer learning and explainable AI, help deliver insights while keeping energy use low.

The project also builds benchmarking tools. These measure how much energy each service or algorithm uses. The goal is to compare and improve energy efficiency across applications.

Real-Life Pilots & Industry Integration

One pilot of the project focuses on green energy. In this case, EDP’s R&D Center leads a “Energy Data Marketplace.” Companies share wind farm data without giving away private details. Machine learning helps make better forecasts while saving energy.

The marketplace uses decentralised learning and smart algorithms. These are designed to use less computing power and tap into clean energy sources like solar or wind. The pilot aims to boost forecast accuracy, stabilize the grid, reduce penalties from wrong bids, and even help producers sell energy more efficiently. It also tests how well competitors can work together using secure shared data models.

Other pilots span smart agriculture, mobility, and banking. They apply AI services in real settings, from optimizing smart farming to managing shared electric bikes, and even detecting bank fraud with privacy-aware AI.

Potential Impact on European Industry

GREEN.DAT.AI could reshape industry. Smarter AI can lower energy bills, boost sustainability scores, and fit better with ESG (Environmental, Social, Governance) standards. Systems that use less power are cheaper to run and maintain.
Shared data spaces spark new value chains. Organizations can monetize data safely and improve services without exposing secrets.
Better forecasting means smoother power grids. That alone cuts waste, ramp-up costs, and reliance on backup fossil fuel plants. Europe could gain a tech edge. Green AI tools can become export products. Their success in one sector can transfer to others. This builds an eco-aware industrial AI culture.

Challenges & Risks

Trade-offs are real. Sometimes energy efficiency means slower models or less accuracy. Companies worry about the costs of changing old systems. Regulations can be tricky. They must balance data privacy, security, and environmental laws.
Green AI in Europe also competes with efforts worldwide. Staying competitive while staying sustainable is a careful balance.

The Road Ahead

Pilots run through 2025. The next steps include publishing open-source tools, releasing evaluation reports, and building partnerships for real-world use. A longer-term aim is to seed sustainability-by-design into AI development, making energy efficiency standard, not optional.

Creating a benchmark system helps, too. It allows future projects to measure progress in a clear way. GREEN.DAT.AI may end up being a foundation for global green-AI policies and tools.

Final Thoughts 

GREEN.DAT.AI shows how green and smart tech can work together. It proves that AI doesn't have to drain energy. It can save it. It puts Europe at the forefront of sustainable AI. The future of industrial AI just got a lot greener.

Frequently Asked Questions (FAQs)

What is the European Project on AI?

The European Project on AI is a group of EU programs, like Green.DAT.AI (launched January 2023), that develop safe, efficient, and sustainable artificial intelligence for industries and society.

What is AI for green energy?

AI for green energy uses smart software to manage renewable power like solar and wind. It predicts demand, improves storage, and reduces waste, making energy cleaner and more reliable.

How is AI used in energy efficiency?

AI helps energy efficiency by monitoring usage, detecting waste, and adjusting systems automatically. It can control heating, cooling, and machines, lowering costs and cutting carbon emissions in real time.