Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/1/76 |
| _version_ | 1818040285254385664 |
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| author | Lukas Lundström Jan Akander |
| author_facet | Lukas Lundström Jan Akander |
| author_sort | Lukas Lundström |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T08:12:06Z |
| format | Article |
| id | doaj.art-2208f83f09ca492da48a8477b7618524 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-12-10T08:12:06Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2208f83f09ca492da48a8477b76185242022-12-22T01:56:33ZengMDPI AGEnergies1996-10732019-12-011317610.3390/en13010076en13010076Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily BuildingsLukas Lundström0Jan Akander1School of Business, Society and Engineering, Mälardalen University, 72123 Västerås, SwedenDepartment of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, 80176 Gävle, SwedenReliable 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.https://www.mdpi.com/1996-1073/13/1/76building energy performanceenergy modelsbayesian calibrationaugmented stochastic state-space modelingiterated extended kalman filteringuncertainty |
| spellingShingle | Lukas Lundström Jan Akander Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings Energies building energy performance energy models bayesian calibration augmented stochastic state-space modeling iterated extended kalman filtering uncertainty |
| title | Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings |
| title_full | Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings |
| title_fullStr | Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings |
| title_full_unstemmed | Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings |
| title_short | Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings |
| title_sort | bayesian calibration with augmented stochastic state space models of district heated multifamily buildings |
| topic | building energy performance energy models bayesian calibration augmented stochastic state-space modeling iterated extended kalman filtering uncertainty |
| url | https://www.mdpi.com/1996-1073/13/1/76 |
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