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.
News & Events
Alfonso Capozzoli, with his article published on AICARR journal “Le potenzialità dell’analisi dati per la gestione energetica degli edifici”, was awarded with a special mention by AICARR (Italian association of air conditioning heating and refrigeration)
On 19/01/2022 Capozzoli Alfonso gave a presentation titled "The penetration of AI in EMS" during the Guidehouse insight webinar: "The Growing Impact of EMS Solutions: The Future of EMS"
Our Lab member Coraci Davide was awarded by IEEE Italy Section for his master thesis on adaptive control strategies for enhancing energy efficiency and comfort in buildings
On 17/06/2021 our Lab coordinator Capozzoli Alfonso gave a presentation titled "BAEDA Lab experience in FDD and ADD in Energy&Buildings" during the expert meeting of ANNEX 81 subtask C.2
Energy and AI Special Issue “AI and Data Science-enabled Applications for Intelligent Building Energy Management” is open for submission!
From June 15th 2021 the special issue “AI and Data Science-enabled Applications for Intelligent Building Energy Management” is open for submission
On 13/04/2021 our Lab member Brandi Silvio gave a lecture titled "Free from model - A novel paradigm for the control of the built environment" in the course "Smart Buildings and Cities" chaired by Prof. Zoltan Nagy (University of Texas at Austin - Intelligent Environment Laboratory)