Reinforcement Learning for Mixing Loop Control with Flow Variable Eligibility Trace

Anders Overgaard, Brian Kongsgaard Nielsen, Carsten Kallesøe, Jan Dimon Bendtsen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

4 Citationer (Scopus)

Abstrakt

Mixing Loops are often used for proper pressurization and temperature control in building thermal systems. Optimal control of the mixing loop maximizes comfort while minimizing cost. To ensure optimal control for mixing loops in a wide range of different buildings with different load conditions, a self learning controller is here proposed. The controller uses Reinforcement Learning with flow variable eligibility trace. The controller is shown to improve performance of the mixing loop control compared to state of the art reinforcement learning and industrial grade controllers. The controller is tested on a hardware in the loop setup for rapid testing of mixing loop control used in building heating.

OriginalsprogEngelsk
TitelIEEE Conference on Control Technology and Applications (CCTA)
Antal sider6
ForlagIEEE
Publikationsdato2019
Sider1043-1048
Artikelnummer8920398
ISBN (Trykt)978-1-7281-2768-2
ISBN (Elektronisk)978-1-7281-2767-5
DOI
StatusUdgivet - 2019
Begivenhed2019 IEEE Conference on Control Technology and Applications (CCTA) - Hong Kong, Kina
Varighed: 19 aug. 201921 aug. 2019

Konference

Konference2019 IEEE Conference on Control Technology and Applications (CCTA)
Land/OmrådeKina
ByHong Kong
Periode19/08/201921/08/2019

Fingeraftryk

Dyk ned i forskningsemnerne om 'Reinforcement Learning for Mixing Loop Control with Flow Variable Eligibility Trace'. Sammen danner de et unikt fingeraftryk.

Citationsformater