Vis enkel innførsel

dc.contributor.authorYu, Xingji
dc.contributor.authorGeorges, Laurent
dc.contributor.authorImsland, Lars Struen
dc.date.accessioned2022-08-08T07:44:16Z
dc.date.available2022-08-08T07:44:16Z
dc.date.created2021-04-14T14:51:47Z
dc.date.issued2021
dc.identifier.citationEnergy and Buildings. 2021, 236 1-17.en_US
dc.identifier.issn0378-7788
dc.identifier.urihttps://hdl.handle.net/11250/3010517
dc.description.abstractGrey-box models are data-driven models where the structure is defined by the physics while the parameters are calibrated using data. Low-order grey-box models of the building envelope are typically used for two main applications. Firstly, they are used as a control model in Model Predictive Control (MPC) where the thermal mass of the building is activated as storage (for instance in demand response). Secondly, they are used to characterize the thermal properties of the building envelope using on-site measurements. The influence of the data pre-treatment on the performance of grey-box models is hardly discussed in the literature. However, in real applications, information about data pre-processing by sensors or data acquisition systems is expected to be limited. Therefore, the influence of the sampling time, low-pass filters and anti-causal shift (also called data labeling) are analyzed for grey-box models in deterministic and stochastic innovation form. The influence on the optimizer performance is also investigated. The datasets are generated from virtual experiments using multi-zone building performance simulations of a residential building (in lightweight wooden construction) heated using different types of excitation signals. Results show that the parameters of deterministic grey-box models are significantly influenced by the training data while the data pre-treatment has a limited impact on the model and optimizer performance. Depending on the training data, the value taken by some parameters is not physically plausible. On the contrary, stochastic models are significantly influenced by the data pre-treatment, especially the sampling time, and less by the training data. The parameters can become non-physical for large sampling times. However, the anti-causal shift proves to be efficient to keep the parameters almost constant with increasing sampling times. Even though the parameter values of the deterministic model are less physically plausible, the simulation performance of deterministic models is higher than using the equivalent stochastic models. These results suggest that deterministic models seem better suited for MPC while stochastic models are better suited for the characterization of thermal properties (if suitable data pre-treatment is applied).en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleData pre-processing and optimization techniques for stochastic and deterministic low-order grey-box models of residential buildingsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderThis article will not be available until April 1, 2023 due to publisher embargo - This manuscript version is made available under the CC-BY-NC-ND 4.0 licenseen_US
dc.source.pagenumber1-17en_US
dc.source.volume236en_US
dc.source.journalEnergy and Buildingsen_US
dc.identifier.doi10.1016/j.enbuild.2021.110775
dc.identifier.cristin1904055
dc.relation.projectNorges forskningsråd: 257660en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal