AI-driven forecasting algorithms for Grid and Consumer friendly Energy Sharing – Societal Readiness pilot

AI-driven forecasting algorithms and machine learning can optimize bidirectional energy transfers involving flexibility services that can help energy communities and jointly acting customers evolve in their role as active participants in the energy system, while ensuring the reduced burdens on DSOs and residual suppliers. Innovations in machine learning forecasting can help balance energy systems and enable active participation in a decentralized, resilient, and digitalized European energy landscape, optimizing self-consumption and reducing energy costs for consumers.

This topic is a Societal-Readiness pilot:

• Proposals should follow the instructions applying to the Societal Readiness pilot, as described in the introduction of the Horizon Europe Main Work Programme 2026-2027 for Climate, Energy and Mobility. They entail the use of an interdisciplinary approach to deepening consideration and responsiveness of R&I activities to societal needs and concerns.
• This topic requires effective contribution of the relevant SSH expertise, including the involvement of SSH experts in the consortium, to meaningfully support Societal Readiness. Specifically, SSH expertise is expected to facilitate the socio-technological interface and enable the design of project objectives with Societal Readiness related activities.

Selected projects are expected to contribute to the BRIDGE initiative [[https://bridge-smart-grid-storage-systems-digital-projects.ec.europa.eu/]] and actively participate in its activities.