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dc.contributor.authorStauffer, Yves
dc.contributor.authorOlivero, Elisa
dc.contributor.authorOnillon, Emmanuel
dc.contributor.authorMahmed, Cyril
dc.contributor.authorLindelöf, David
dc.date.accessioned2022-02-14T17:08:06Z
dc.date.available2022-02-14T17:08:06Z
dc.date.issued2017-09-01
dc.identifier.citationCISBAT 2017 International ConferenceFuture Buildings & Districts – Energy Efficiency from Nano to Urban Scale, vol. 122, pp. 127-132
dc.identifier.issn1876-6102
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/883
dc.description.abstractWe present the field test results on a novel model-predictive controller for HVAC systems, which minimizes the operational costs of the air-treatment unit while guaranteeing indoor comfort. Unlike traditional control systems, which usually ignore the building's physics and cannot use weather forecasts, this controller features an adaptive model of the controlled space and uses weather forecasts from a national weather forecasting service. We tested this controller on two real, occupied buildings during the summer of 2017 and compared its performance with the existing control system. The system performs at least as well as the original controller, while reducing the operational costs by about 20%.
dc.titleNeuroCool: field tests of an adaptive, model-predictive controller for HVAC systems
dc.typeJournal Article
dc.type.csemdivisionsDiv-E
dc.type.csemresearchareasDigital Energy
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1876610217329144
dc.identifier.doi10.1016/j.egypro.2017.07.316


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