GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems, while reducing the environmental impact of data management processes. GREEN.DAT.AI will demonstrate the efficiencies of the new 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. The ambition is to exploit mature (TRL5 or higher) solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL - ready platform, and a validated go-to-market TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover 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/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting. The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.

Storage is becoming one of the most critical assets for modern energy systems. At the same time storage is a very diverse universe of solutions and technologies with very different characteristics. InterSTORE plans to address this complexity by developing an innovative middleware that, while virtualising the storage technology, will simplify its use from the point of view of integration platform thanks to a technology agnostic approach.The middleware will facilitate the integration of storage creating an independence from hardware solutions which are critical from customer perspectives to avoid vendor lock-in solutions. It will also facilitate its use from a monetisation perspective making sure that more investments in storage are enabled. InterSTORE middleware will be released as a full open-source product integrating a set of already available standards and protocols in a coherent and advanced architecture.The new InterSTORE solution will be tested and validated in a laboratory environment with the goal to develop testing software to be adopted in the future for interoperability certification. The middleware solution will also be integrated both in open source and commercial platforms that will be integrated in 4 real life demos to enhance the flexibility platforms and analyze the user acceptance and economic value of the new solution. Moreover, new and legacy systems will be taken into account. InterSTORE also has a strong focus towards impact. The solution will become part of existing commercial platforms ensuring that a real adoption will be deployed beyond the end of the project. The consortium will ensure that software maintenance will continue beyond the end of the project with the support of Linux Foundation Energy and that the work done will be considered by on-going standardization activities. SSH experts will perform an impact analysis to maximize the economic impact adopting innovative approaches to data monetization in the context of data spaces.