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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 [10.26868/25222708.2019.210614].
| 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
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