PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishEfficient control of Heating, Ventilation and Air Conditioning systems can lead to great reduction in energy consumption. This can be achieved by new data-driven control algorithms based on Reinforcement Learning (RL). In this work Dynamic Simulation is coupled with a model-free RL algorithm to study its performance in terms of energy saving and thermal comfort in a realistic scenario. Two models are derived from the DOE Supermarket Reference Building for two climate locations. The simulations performed show a reduction between 5.4% and 9.4% in primary energy consumption for the two locations, guaranteeing the same thermal comfort of state-ofthe-art controls.
A Reinforced Learning (RL) algorithm for optimal control of HVAC: Comparison with a standard PI control for the supermarket DOE reference building / Ballesio, Stefano; Castiglione, Fabio; Fabrizio, Enrico; Mastropietro, Antonio. - (2020), pp. 1412-1419. ((Intervento presentato al convegno BS 2019 tenutosi a Roma nel 2-4 September 2019.
| Titolo: | A Reinforced Learning (RL) algorithm for optimal control of HVAC: Comparison with a standard PI control for the supermarket DOE reference building | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | Efficient control of Heating, Ventilation and Air Conditioning systems can lead to great reduction ...in energy consumption. This can be achieved by new data-driven control algorithms based on Reinforcement Learning (RL). In this work Dynamic Simulation is coupled with a model-free RL algorithm to study its performance in terms of energy saving and thermal comfort in a realistic scenario. Two models are derived from the DOE Supermarket Reference Building for two climate locations. The simulations performed show a reduction between 5.4% and 9.4% in primary energy consumption for the two locations, guaranteeing the same thermal comfort of state-ofthe-art controls. | |
| ISBN: | 978-1-7750520-1-2 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| BS2019_210614(2).pdf | 2a Post-print versione editoriale / Version of Record | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2816559
Copyright © 2022