Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Xingbin Lin"'
Publikováno v:
Buildings, Vol 13, Iss 3, p 580 (2023)
Reinforcement learning (RL) is being gradually applied in the control of heating, ventilation and air-conditioning (HVAC) systems to learn the optimal control sequences for energy savings. However, due to the “trial and error” issue, the output s
Externí odkaz:
https://doaj.org/article/a8b08e5e1ad74d6c83087d7fc70cbecf
Publikováno v:
Buildings, Vol 13, Iss 2, p 314 (2023)
The use of machine-learning algorithms in optimizing the energy efficiency of HVAC systems has been widely studied in recent years. Previous research has focused mainly on data-driven model predictive controls and reinforcement learning. Both approac
Externí odkaz:
https://doaj.org/article/eb4b469a70754697a19d5f82039bfc38
Publikováno v:
ASHRAE Transactions. 2014, Vol. 120 Issue 1, p81-87. 7p. 1 Chart, 6 Graphs.
Autor:
Josephine Lau, Xingbin Lin
Publikováno v:
Science and Technology for the Built Environment. 21:1100-1108
In Part 1 of this article, a CO2-based dynamic reset was proposed and evaluated as an energy-saving demand-controlled ventilation strategy by reducing the outdoor airflow rate when the occupancy is under design occupancy. Further energy-saving potent
Autor:
Josephine Lau, Xingbin Lin
Publikováno v:
HVAC&R Research. 20:875-888
Demand controlled ventilation (DCV) is used to reduce the system outdoor airflow (OA) when the occupancy of the system is under design occupancy. However, the new versions of ASHRAE Standard 62.1-2010 make DCV more difficult to implement for multiple
Publikováno v:
Hydrocephalus ISBN: 9784431681588
In spite of recent developments in diagnostic procedures, there are, as yet, no absolute criteria for making a definitive diagnosis of NPH. Transcranial Doppier (TCD) data concerning vascular reactivity obtained by hypercapnia, together with temporar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc9c785e8e1d35db2255453d0df5e029
https://doi.org/10.1007/978-4-431-68156-4_63
https://doi.org/10.1007/978-4-431-68156-4_63