AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector
Alexandros Menelaos Tzortzis, Georgios Kormpakis, Sotiris Pelekis, Ariadni Michalitsi-Psarrou, Evangelos Karakolis, Christos Ntanos, Dimitris Askounis
TL;DR
AI4EF provides a modular, AI-driven decision-support platform for energy efficiency in buildings, integrating retrofit and PV installation analyses with an MLOps-backed Training Playground and secure data sharing via the Enershare Data Space. The architecture combines a Docker-based microservices stack, Keycloak-enabled identity management, and a React frontend to deliver user-friendly, role-specific capabilities. Key contributions include open-source accessibility, user-driven model training, real-world validation with LEIF-derived data, and alignment with European data ecosystems and data-sharing standards. The work demonstrates substantial potential to accelerate energy retrofit decisions, optimize renewable integration, and support policy and investment in a scalable, interoperable, data-driven framework.
Abstract
AI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF enables stakeholders such as public sector representatives, energy consultants, and building owners to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the platform incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. Its compatibility with open-source identity management, Keycloak, enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an architectural overview of AI4EF, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI).