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Bayesian calibration with augmented stochastic state-space models of district-heated multifamily buildings
Mälardalens högskola.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, Energy Systems and Building Technology.ORCID iD: 0000-0001-9076-0801
2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 13, no 1, article id 76Article in journal (Refereed) Published
Abstract [en]

Reliable energy models are needed to determine building energy performance. Relatively detailed energy models can be auto-generated based on 3D shape representations of existing buildings. However, parameters describing thermal performance of the building fabric, the technical systems, and occupant behavior are usually not readily available. Calibration with on-site measurements is needed to obtain reliable energy models that can offer insight into buildings' actual energy performances. Here, we present an energy model that is suitable for district-heated multifamily buildings, based on a 14-node thermal network implementation of the ISO 52016-1:2017 standard. To better account for modeling approximations and noisy inputs, the model is converted to a stochastic state-space model and augmented with four additional disturbance state variables. Uncertainty models are developed for the inputs solar heat gains, internal heat gains, and domestic hot water use. An iterated extended Kalman filtering algorithm is employed to enable nonlinear state estimation. A Bayesian calibration procedure is employed to enable assessment of parameter uncertainty and incorporation of regulating prior knowledge. A case study is presented to evaluate the performance of the developed framework: parameter estimation with both dynamic Hamiltonian Monte Carlo sampling and penalized maximum likelihood estimation, the behavior of the filtering algorithm, the impact of different commonly occurring data sources for domestic hot water use, and the impact of indoor air temperature readings.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 13, no 1, article id 76
Keywords [en]
Augmented stochastic state-space modeling, Bayesian calibration, Building energy performance, Energy models, Iterated Extended Kalman Filtering, Uncertainty, Buildings, Calibration, District heating, Energy efficiency, Extended Kalman filters, Hamiltonians, Hot water distribution systems, Maximum likelihood estimation, Monte Carlo methods, State space methods, Stochastic systems, Uncertainty analysis, Water, Energy model, Extended Kalman filtering, State - space models, Stochastic models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hig:diva-31401DOI: 10.3390/en13010076Scopus ID: 2-s2.0-85077310649OAI: oai:DiVA.org:hig-31401DiVA, id: diva2:1384946
Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2020-01-13Bibliographically approved

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Akander, Jan

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