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A Bayesian approach with urban-scale energy model to calibrate building energy consumption for space heating: A case study of application in Beijing

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  • Na, Wei
  • Wang, Mingming

Abstract

Urban-scale energy model can be a crucial tool for evaluating and in turn propelling the implementation of energy efficiency and low carbon programs for buildings and cites. A variety of disputes on integrity and correctness lie in basic statistical data of China's building energy use. Inadequate information and performance gap caused by input uncertainties are the common obstacles to produce reliable energy modelling results. This paper develops a bottom-up approach to depict energy use intensity (EUI) for space heating of building in the urban level accurately, even if the data resource is inadequate and imprecise or the data are influenced by uncertain issues. A hybrid probability model is developed and the model parameters are calibrated by Bayesian inference and Markov Chain Monte Carlo simulation using data set of meter reading from 2062 stochastic-sampled heating substations in Beijing. Preparation and simulation efforts of using this energy model are discussed, following this framework and adjusting according to local climate conditions, typological building characteristics and morphological urban-scale parameters. The results show that the approach is reliable and efficient to calibrate parameters in building energy models. The urban-scale benchmarks of EUI for space heating in Beijing is successfully performed to demonstrate the proposed methodology.

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  • Na, Wei & Wang, Mingming, 2022. "A Bayesian approach with urban-scale energy model to calibrate building energy consumption for space heating: A case study of application in Beijing," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222002444
    DOI: 10.1016/j.energy.2022.123341
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    References listed on IDEAS

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    1. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    2. Raillon, L. & Ghiaus, C., 2018. "An efficient Bayesian experimental calibration of dynamic thermal models," Energy, Elsevier, vol. 152(C), pages 818-833.
    3. Cerezo Davila, Carlos & Reinhart, Christoph F. & Bemis, Jamie L., 2016. "Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets," Energy, Elsevier, vol. 117(P1), pages 237-250.
    4. Mingshun Zhang & Xuan Ge & Ya Zhao & Chun Xia-Bauer, 2019. "Creating Statistics for China’s Building Energy Consumption Using an Adapted Energy Balance Sheet," Energies, MDPI, vol. 12(22), pages 1-15, November.
    5. Si Chen & Daniel Friedrich & Zhibin Yu & James Yu, 2019. "District Heating Network Demand Prediction Using a Physics-Based Energy Model with a Bayesian Approach for Parameter Calibration," Energies, MDPI, vol. 12(18), pages 1-19, September.
    6. Matthew Plumlee, 2017. "Bayesian Calibration of Inexact Computer Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1274-1285, July.
    7. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    8. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    9. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
    10. Lund, Henrik & Andersen, Anders N. & Østergaard, Poul Alberg & Mathiesen, Brian Vad & Connolly, David, 2012. "From electricity smart grids to smart energy systems – A market operation based approach and understanding," Energy, Elsevier, vol. 42(1), pages 96-102.
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    1. Heidenthaler, Daniel & Deng, Yingwen & Leeb, Markus & Grobbauer, Michael & Kranzl, Lukas & Seiwald, Lena & Mascherbauer, Philipp & Reindl, Patricia & Bednar, Thomas, 2023. "Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts," Energy, Elsevier, vol. 278(PB).
    2. Zou, Chenchen & Ma, Minda & Zhou, Nan & Feng, Wei & You, Kairui & Zhang, Shufan, 2023. "Toward carbon free by 2060: A decarbonization roadmap of operational residential buildings in China," Energy, Elsevier, vol. 277(C).

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