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Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling

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  • Martín Mosteiro-Romero

    (Architecture and Building Systems, ETH Zurich, Stefano-Franscini-Platz 1, 8093 Zurich, Switzerland
    Current address: Department of Architecture, School of Design and Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore.)

  • Arno Schlueter

    (Architecture and Building Systems, ETH Zurich, Stefano-Franscini-Platz 1, 8093 Zurich, Switzerland)

Abstract

Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and thermal supply system sizing at the district scale. In order to analyze the effects of the former, three different methods of occupant modeling were implemented in a UBEM. The effects of the urban heat island on system sizing were studied through the use of measured temperature data from a weather station in the case study district compared to measured data from a national weather station. The methods developed were used to assess the sizing and costs of centralized and decentralized technologies for a case study in central Zurich, Switzerland. The choice of occupant modeling approach was found to affect the district’s total annualized costs for space heating and cooling by ±5%, whereas for the costs of electricity the variation was ±8%. Regarding outdoor temperature, the effects on the heating demands proved be negligible, however the costs of the cooling alternatives were found to vary by about 4% at the district scale due to the effect of urban climate, for individual buildings this deviation was as high as 40%.

Suggested Citation

  • Martín Mosteiro-Romero & Arno Schlueter, 2021. "Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling," Energies, MDPI, vol. 14(8), pages 1-30, April.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2295-:d:539015
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    References listed on IDEAS

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    1. Toparlar, Y. & Blocken, B. & Maiheu, B. & van Heijst, G.J.F., 2018. "Impact of urban microclimate on summertime building cooling demand: A parametric analysis for Antwerp, Belgium," Applied Energy, Elsevier, vol. 228(C), pages 852-872.
    2. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    3. Santos, Luis Guilherme Resende & Afshari, Afshin & Norford, Leslie K. & Mao, Jiachen, 2018. "Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect," Applied Energy, Elsevier, vol. 215(C), pages 31-40.
    4. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    5. Happle, Gabriel & Fonseca, Jimeno A. & Schlueter, Arno, 2020. "Impacts of diversity in commercial building occupancy profiles on district energy demand and supply," Applied Energy, Elsevier, vol. 277(C).
    6. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    7. Alaia Sola & Cristina Corchero & Jaume Salom & Manel Sanmarti, 2018. "Simulation Tools to Build Urban-Scale Energy Models: A Review," Energies, MDPI, vol. 11(12), pages 1-24, November.
    8. Edward Barbour & Carlos Cerezo Davila & Siddharth Gupta & Christoph Reinhart & Jasleen Kaur & Marta C. González, 2019. "Planning for sustainable cities by estimating building occupancy with mobile phones," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    9. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    10. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
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    1. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.

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