English
Controllo della temperatura negli edifici: č possibile imparare ad imparare?
Luca Ferrarini - Politecnico di Milano - DEIB
Presentazione in lingua inglese
Progettazione e Implementazione di un controllo con Meta-Reinforcement Learning
Meta-RL Control Architecture
Introduction
Machine Learning - Overview
Supervised Learning: the ML algorithm learns an input- output mapping from a labelled dataset ?learn the model.
Unsupervised Learning: the ML algorithm is provided with unlabelled data and learns the underlying structure or distribution of the data ?learn the patterns in the data.
Reinforcement Learning: the ML algorithm learns which actions to take in order to maximize a numerical reward signal ?learn to control
A Reinforcement Learning (RL) agent aims to learn the optimal way to accomplish a task through repeated interactions with its environment, by evaluating the longtermvalue of its actions.
Approaches to Temperature Control
- Classical methods (PID)
- Advanced control (MPC)
- Data-Driven control (RL)
How to face uncertainty in the building dynamics?
Meta-Reinforcement Learning
Problem statement:
Design a temperature control system that rapidly adapts to uncertain building thermal dynamics - Meta-Reinforcement Learning (Meta-RL)
Meta Learning: provide machines with the skill to learn how to learn.
Characteristics:
- Learns over a distribution of environments
- Training offline
- fast adaptation to new / changing systems
Case Study Building 25
(in the PDF)
Machine Learning - Overview
Supervised Learning: the ML algorithm learns an input- output mapping from a labelled dataset ?learn the model.
Unsupervised Learning: the ML algorithm is provided with unlabelled data and learns the underlying structure or distribution of the data ?learn the patterns in the data.
Reinforcement Learning: the ML algorithm learns which actions to take in order to maximize a numerical reward signal ?learn to control
A Reinforcement Learning (RL) agent aims to learn the optimal way to accomplish a task through repeated interactions with its environment, by evaluating the longtermvalue of its actions.
Approaches to Temperature Control
- Classical methods (PID)
- Advanced control (MPC)
- Data-Driven control (RL)
How to face uncertainty in the building dynamics?
Meta-Reinforcement Learning
Problem statement:
Design a temperature control system that rapidly adapts to uncertain building thermal dynamics - Meta-Reinforcement Learning (Meta-RL)
Meta Learning: provide machines with the skill to learn how to learn.
Characteristics:
- Learns over a distribution of environments
- Training offline
- fast adaptation to new / changing systems
Case Study Building 25
(in the PDF)
Leggi tutto
Fonte: SAVE ottobre 2025 Le novitą dello Smart Building per la gestione intelligente dell'edificio
Settori: AI per industria, Building automation, Domotica, Efficienza energetica edifici, Efficienza energetica immobili terziario e commerciale, Efficienza energetica industriale, Intelligenza artificiale, Smart City
Mercati: Edilizia, Manutenzione industriale
Parole chiave: AI Intelligenza artificiale per Efficienza energetica, Smart building
