IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2019i1p76-d300890.html
   My bibliography  Save this article

Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings

Author

Listed:
  • Lukas Lundström

    (School of Business, Society and Engineering, Mälardalen University, 72123 Västerås, Sweden)

  • Jan Akander

    (Department of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, 80176 Gävle, Sweden)

Abstract

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.

Suggested Citation

  • Lukas Lundström & Jan Akander, 2019. "Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings," Energies, MDPI, vol. 13(1), pages 1-28, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:76-:d:300890
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/1/76/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/1/76/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Raillon, L. & Ghiaus, C., 2018. "An efficient Bayesian experimental calibration of dynamic thermal models," Energy, Elsevier, vol. 152(C), pages 818-833.
    3. Hedegaard, Rasmus Elbæk & Kristensen, Martin Heine & Pedersen, Theis Heidmann & Brun, Adam & Petersen, Steffen, 2019. "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response," Applied Energy, Elsevier, vol. 242(C), pages 181-204.
    4. Lukas Lundström & Jan Akander & Jesús Zambrano, 2019. "Development of a Space Heating Model Suitable for the Automated Model Generation of Existing Multifamily Buildings—A Case Study in Nordic Climate," Energies, MDPI, vol. 12(3), pages 1-27, February.
    5. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    6. Deuk-Woo Kim & Cheol-Soo Park, 2017. "Application of Kalman Filter for Estimating a Process Disturbance in a Building Space," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    7. Jakob Carlander & Kristina Trygg & Bahram Moshfegh, 2019. "Integration of Measurements and Time Diaries as Complementary Measures to Improve Resolution of BES," Energies, MDPI, vol. 12(11), pages 1-29, May.
    8. Marieline Senave & Staf Roels & Stijn Verbeke & Evi Lambie & Dirk Saelens, 2019. "Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis," Energies, MDPI, vol. 12(17), pages 1-29, August.
    9. Kensby, Johan & Trüschel, Anders & Dalenbäck, Jan-Olof, 2015. "Potential of residential buildings as thermal energy storage in district heating systems – Results from a pilot test," Applied Energy, Elsevier, vol. 137(C), pages 773-781.
    10. Werner, Sven, 2017. "District heating and cooling in Sweden," Energy, Elsevier, vol. 126(C), pages 419-429.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    2. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    3. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    4. Piotr Michalak, 2023. "Simulation and Experimental Study on the Use of Ventilation Air for Space Heating of a Room in a Low-Energy Building," Energies, MDPI, vol. 16(8), pages 1-17, April.
    5. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    7. Lukas Lundström & Jan Akander & Jesús Zambrano, 2019. "Development of a Space Heating Model Suitable for the Automated Model Generation of Existing Multifamily Buildings—A Case Study in Nordic Climate," Energies, MDPI, vol. 12(3), pages 1-27, February.
    8. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    9. Wandong Zheng & Jay J. Hennessy & Hailong Li, 2020. "Reducing renewable power curtailment and CO2 emissions in China through district heating storage," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(1), January.
    10. Marco Massano & Edoardo Patti & Enrico Macii & Andrea Acquaviva & Lorenzo Bottaccioli, 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings," Energies, MDPI, vol. 13(8), pages 1-17, April.
    11. Guelpa, Elisa, 2021. "Impact of thermal masses on the peak load in district heating systems," Energy, Elsevier, vol. 214(C).
    12. Arkar, C. & Žižak, T. & Domjan, S. & Medved, S., 2020. "Dynamic parametric models for the holistic evaluation of semi-transparent photovoltaic/thermal façade with latent storage inserts," Applied Energy, Elsevier, vol. 280(C).
    13. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    14. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    15. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    16. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    17. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    18. Piotr Michalak, 2021. "Modelling of Solar Irradiance Incident on Building Envelopes in Polish Climatic Conditions: The Impact on Energy Performance Indicators of Residential Buildings," Energies, MDPI, vol. 14(14), pages 1-27, July.
    19. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    20. Khosravi, Fatemeh & Lowes, Richard & Ugalde-Loo, Carlos E., 2023. "Cooling is hotting up in the UK," Energy Policy, Elsevier, vol. 174(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:76-:d:300890. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.