AI-DAPT brings forward a data-centric mentality in AI, that is effectively fused with a model-centric, science-guided approach, across the complete lifecycle of AI-Ops, by introducing end-to-end automation and AI-based systematic methods to support the design, the execution, the observability and the lifecycle management of robust, intelligent and scalable data-AI pipelines that continuously learn and adapt based on their context. AI-DAPT will design a novel AI-Ops / intelligent pipeline lifecycle framework cross-cutting the different business, legal/ethics, data, AI logic/models, and system requirements while always ensuring a human-in-the-loop (HITL) approach across five axis: “Data Design for AI”, “Data Nurturning for AI”, “Data Generation for AI”, “Model Delivery for AI”, “Data-Model Optimization for AI”. AI-DAPT will contribute to the current research and advance the state-of-the-art techniques and technologies across a number of research paths, including sophisticated Explainable AI (XAI)-driven data operations from purposing, harvesting/mining, exploration, documentation and valuation to interoperability, annotation, cleaning, augmentation and bias detection; collaborative feature engineering minimizing the data where appropriate; adaptive AI for model retraining purposes. Overall, AI-DAPT aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions. In order to demonstrate the actual innovation and added value that can be derived through the AI-DAPT scientific advancements, the AI-DAPT results will be validated in two, interlinked axes: I. Through their actual application to address real-life problems in four (4) representative industries: Health, Robotics, Energy, and Manufacturing; II. Through their integration in different AI solutions, either open source or commercial, that are currently available in the market.

The increasing electrification of heat and transport coupled with larger RESs deployment of decentralized RESs is disclosing new additional opportunities for demand response. However DR potential has been exploited so far to a very limited extent at end consumer residential level, due to technologies immaturity, regulatory fuzziness, distorted business framework preventing end consumers to capture an appropriate value. To cope with the above challenges, BRIGHT will leverage on a participatory co-creation process to bring individual consumers center stage to deliver a multi-layered community-centred cross-domain adaptable multi-timescale DR supporting framework which combines social-science-driven user experience design for user behavior motivations and monetary/non-monetary incentive design, Digital Twins models for improved consumer predictability, multi-layered P2P DLT/blockchain/smart contracts based semi-decentralized VPPs for capturing intra-community interaction dynamics, value stacking flexibility management algorithms and other AI data-driven energy and-non energy services at the interplay among energy (power, heat, gas), mobility, health (comfort), smart home (AAL, personal safety). The proposed approach and the underlying enablers will be deployed and validated in 4 demo-sites across 4 EU countries where around 1000 mostly residential consumers will be engaged along a variety of different community configurations (LEC, CEC, Virtual Energy Communities, Communities on the Move).

EU-DREAM brings together a group of preeminent energy industry and research partners focused on accelerating innovation in digital tools and promoting the effective uptake of digital services. EU-DREAM is aligned with the EU Action Plan on the Digitalisation of the Energy System as it proposes to develop the next generation of energy services, solutions and products that really work for energy consumers, fully tested and demonstrated in 6 LLs in 6 EU countries (Portugal, Belgium, Italy, Ireland, Greece and Denmark). EU-DREAM will address the barriers, motivations, and drivers from the consumer’s perspective, intertwining the new technological developments with SSH expertise. All EU-DREAM technical solutions will produce high-level TRL 6-7 results by the end of the project.

Efforts to induce energy-friendly behavior from end users through behavioral interventions are characterized by lack of customer personalization (“one-size-fits-all interventions”), partial understanding about how different interventions interact with each other and contrasting evidence about their effectiveness, as a result of poor testing under real world conditions. The NUDGE project has been conceived to unleash the potential of behavioral interventions for long-lasting energy efficiency behavior changes, paving the way to the generalized use of such interventions as a worthy addition to the policy-making toolbox. We take a mixed approach to the consumer analysis and intervention design tasks combining surveys and field trials. Firmly rooted in behavioral science methods, we study individual psychological and contextual variables underlying consumers’ behavior to tailor the design of behavioral interventions for them, with a clear bias towards interventions of the nudging type. The designed interventions are compared against traditional ones in field trials (pilots) in five different EU states, exhibiting striking diversity in terms of innovative energy usage scenarios (e.g., PV production for EV charging, DR for natural gas), demographic and socio-economic variables of the involved populations, mediation platforms for operationalizing the intervention (smart mobile apps, dashboards, web portals, educational material and intergenerational learning practices). We are a multidisciplinary team of 10 partners marking solid expertise in behavioral science, (mobile) user interface and policy design; synergies of different stakeholders (energy providers/cooperative/communities, consumer associations, technology providers); and extensive networks of expert groups, industrial & consumer associations amplifying our potential for tangible impact on policy-making at all levels.

EU countries have drawn up strategies reflected in their National Energy Efficiency Action Plans that include provision of an overview of the country's national building stock, identification of key policies that the country intends to use to stimulate renovations and provision of an estimate of the expected energy savings that will result from renovations. Despite the increase in the use of energy and the evident environmental benefits of having more share of renewable energy sources (RES) in buildings, the adoption of both energy efficiency measures and RES is highly influenced by its cost and the impact on occupants’ comfort. The real implementation of actions to reduce energy consumption in buildings is confronted with the complexity of managing their internal energy systems, the overall target of cost savings and the respect of the levels of comfort expected by the buildings occupants. The BIGG project aims at demonstrating the application of big data technologies and data analytic techniques for the complete buildings life-cycle of more than 4000 buildings in 6 large-scale pilot test-beds, achieved by: 1) The Open Source BIGG Data Reference Architecture 4 Buildings for collection/funneling, processing and exchanging data from different sources (smart-meters, sensors, BMS, existing data sets); 2) An interoperable buildings data specification, BIGG Standard Data Model 4 Buildings, based on the combination of elements from existing frameworks and EC directives, such as SAREF, INSPIRE, BIM, EPCHub that will be enhanced to reach full interoperability of building data; 3) An extensible, open, cloud-based BIGG Data Analytics Toolbox of service modules for batch and real-time analytics that supports a wide range of services, new business models and support reliable and effective policy-making. These solutions will be deployed and tested cross pilot and country validation of at least two business scenarios in Spain and Greece.