BAEDA is a research lab in TEBE group at Department of Energy of Politecnico di Torino aimed to address the challenges posed by digitalization process and penetration of data analytics technologies in the field of energy and buildings. The Lab conducts research to support the transition towards novel paradigms of energy management for intelligent buildings in smart energy grids.
Energy Management and Information Systems (EMIS) in buildings are harnessing the benefits of artificial intelligence development for extracting useful knowledge from monitoring data and enabling the identification of ready-to-implement smart control strategies and energy conservation measures.
The research activities of the Lab are structured on three main systems at the basis of advanced EMIS:
- Energy Information Systems (EIS) including informative solutions aimed at deeply characterizing and modeling the building energy behaviour, assessing the impact of energy management strategies, benchmarking the energy performance at different scales, and implementing predictive management strategies to support the decision-making process.
- Fault Detection and Diagnosis (FDD) consisting of informative solutions aimed to automatically identify faults and/or anomalies in the operation of energy systems (e.g., HVAC systems) and to promptly diagnose the causes of their occurrence.
- Automated System Optimization (ASO) including predictive and adaptive control solutions to optimise the settings of building energy systems considering the trade-off between multiple and contrasting objectives for enhancing energy flexibility, renewable energy integration and building performances.
The Lab conceives and designs innovative EMIS solutions and develops mockups of energy analytics "applications", built on top of widespread software infrastructures in cloud environment or edge-computing devices.
The growing penetration of ICT (Information and Communication Technologies) and IoT (Internet of Things) technologies are strongly influencing the energy and building sector introducing new challenges and paradigms. In this context BAEDA Lab has gained specific skills and expertises on:
- Data-driven building energy management;
- Energy data analytics technologies;
- Smart data-driven control of building energy systems and active envelope components;
- Co-simulation environment for the assessment of predictive building energy management strategies;
- Physical and data-driven modeling of digital twins for the built environment and energy systems;
- Data-driven modeling of occupancy patterns and occupant behaviour in buildings;
- Development of scalable, automated and cloud-based applications for energy management in buildings;
- Modeling of building-to-building and building-to-grid interactions.
Explore our Research activities and our Github!
News & Events
Second PRIN UTMOST FDD project newsletter: Transferring hybrid fault detection and diagnosis strategies in HVAC Systems
A study developed within the PRIN 2022 MUR - UTMOST FDD project investigates how transfer learning techniques can support the deployment of hybrid fault detection and diagnosis strategies for HVAC systems when only limited operational data are available.
First PRIN UTMOST FDD project newsletter: Hybrid artificial intelligence for automated fault detection in HVAC systems
A recent study developed within the PRIN 2022 MUR - UTMOST FDD project proposes a hybrid fault detection and diagnosis framework for HVAC systems that combines machine learning and expert knowledge using Bayesian Networks. The approach demonstrates high diagnostic accuracy and strong potential for practical deployment in building energy management systems.
PRIN UTMOST FDD project - conference plan
Throughout the duration of the the PRIN 2022 MUR - UTMOST FDD project, the research units have actively contributed to the dissemination of the project results by presenting their work at national and international scientific conferences. Below it is possible to consult the updated conference plan of the project, together with periodic updates on the scientific results presented and disseminated by the research units.
Second Workshop in the framework of the project PRIN 2022 MUR - UTMOST FDD
On 17/06/2025 prof. Jin Wen will give a talk titled "Harnessing data and AI for Fault Detection and Diagnosis in Smart Buildings" at Politecnico di Torino within a workshop organized in the framework of the project PRIN 2022 MUR - UTMOST FDD - an aUToMated, Open, Scalable and Transparent Fault Detection and Diagnosis process for air-handling units based on a hybrid expert and artificial intelligence approach.
First Workshop in the framework of the project PRIN 2022 MUR - UTMOST FDD
On 23 April 2025, the first workshop of the PRIN 2022 MUR - UTMOST FDD project was held at the RIAS Laboratory of the University of Campania “Luigi Vanvitelli”. The event brought together researchers and doctoral students to present the objectives, methodologies and expected impacts of the project on hybrid fault detection and diagnosis strategies for air handling units.
Our Lab coordinator Alfonso Capozzoli at presentation of GBC position paper
On 26/11/2024, Capozzoli Alfonso joined in Milan the Conference to present the new GBC position paper on digitization of the construction sector in support of decarbonization goals.
EURAC Research and BAEDA Lab release the Python library BrickLLM
Energy Efficient Building group at EURAC Research and BAEDA Lab release the Python library brickllm, allowing the generation of semantic metadata models of the building using Brick ontology
Sabrina Savino wins the AIDDA 2024 award
Our Lab member Savino Sabrina was awarded by AIDDA PVA Section for her research on AI-based multi-agent systems for high dimensional control problems for energy management in buildings.