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Balancing envelope and heating system parameters for zero emissions retrofit using building sensor data

Author

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  • Nagy, Zoltán
  • Rossi, Dino
  • Hersberger, Christian
  • Irigoyen, Silvia Domingo
  • Miller, Clayton
  • Schlueter, Arno

Abstract

Retrofit measures are an effective means to improve both the heating energy and carbon footprint of a building. On one hand, reducing the losses through the envelope reduces the energy consumption. On the other hand, updating the heating from a fossil-fuel based system to an emission-free one bears the potential for CO2-emission free operation. The latter can be achieved if the supply temperature of the heating system can be sufficiently reduced, such that the operation of a heat pump with a high coefficient of performance becomes feasible. For this, typically the heating area is increased to facilitate the heat transfer. Qualitatively, it is understood that increasing the heating area and improving the insulation of the envelope allows one to lower the supply temperature. However, it is unclear how these improvements relate to each other, or what their individual or combined effect is. In this research, we present a steady-state model to illustrate the impact of retrofit measures on the supply temperature. The model requires the determination of two dimensionless parameters, as well as an estimate for the thermal transmittance (U-value) of the envelope. For this, we developed a flexible, low-cost sensor network. We apply our model to a real retrofit scenario of a historically listed building in Zurich, Switzerland, and show that the current state of the building is already suitable for a low temperature heating system. The findings of our model are confirmed by a calibrated dynamic building simulation. The proposed model provides a means to relate energy savings to reduction of green house gases, and, thus to reduce the CO2 footprint of the building stock.

Suggested Citation

  • Nagy, Zoltán & Rossi, Dino & Hersberger, Christian & Irigoyen, Silvia Domingo & Miller, Clayton & Schlueter, Arno, 2014. "Balancing envelope and heating system parameters for zero emissions retrofit using building sensor data," Applied Energy, Elsevier, vol. 131(C), pages 56-66.
  • Handle: RePEc:eee:appene:v:131:y:2014:i:c:p:56-66
    DOI: 10.1016/j.apenergy.2014.06.024
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    References listed on IDEAS

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    1. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2013. "Zero energy buildings and sustainable development implications – A review," Energy, Elsevier, vol. 54(C), pages 1-10.
    2. Chua, K.J. & Chou, S.K. & Yang, W.M., 2010. "Advances in heat pump systems: A review," Applied Energy, Elsevier, vol. 87(12), pages 3611-3624, December.
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    2. Yongtao Tan & Chenyang Shuai & Tian Wang, 2018. "Critical Success Factors (CSFs) for the Adaptive Reuse of Industrial Buildings in Hong Kong," IJERPH, MDPI, vol. 15(7), pages 1-19, July.
    3. Harmathy, Norbert & Magyar, Zoltán & Folić, Radomir, 2016. "Multi-criterion optimization of building envelope in the function of indoor illumination quality towards overall energy performance improvement," Energy, Elsevier, vol. 114(C), pages 302-317.
    4. Jangsten, M. & Kensby, J. & Dalenbäck, J.-O. & Trüschel, A., 2017. "Survey of radiator temperatures in buildings supplied by district heating," Energy, Elsevier, vol. 137(C), pages 292-301.
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    6. Sanhudo, Luís & Ramos, Nuno M.M. & Poças Martins, João & Almeida, Ricardo M.S.F. & Barreira, Eva & Simões, M. Lurdes & Cardoso, Vítor, 2018. "Building information modeling for energy retrofitting – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 249-260.
    7. Prek, Matjaž & Krese, Gorazd, 2018. "Experimental analysis of an improved regulation concept for multi-panel heating radiators: Proof-of-concept," Energy, Elsevier, vol. 161(C), pages 52-59.
    8. Galvin, Ray & Sunikka-Blank, Minna, 2016. "Quantification of (p)rebound effects in retrofit policies – Why does it matter?," Energy, Elsevier, vol. 95(C), pages 415-424.
    9. Krese, Gorazd & Lampret, Žiga & Butala, Vincenc & Prek, Matjaž, 2018. "Determination of a Building's balance point temperature as an energy characteristic," Energy, Elsevier, vol. 165(PB), pages 1034-1049.
    10. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    11. Müller, Dario & Chartouni, Daniel, 2022. "Implications on EROI and climate change of introducing Li-ion batteries to residential PV systems," Applied Energy, Elsevier, vol. 326(C).
    12. Lämmle, Manuel & Bongs, Constanze & Wapler, Jeannette & Günther, Danny & Hess, Stefan & Kropp, Michael & Herkel, Sebastian, 2022. "Performance of air and ground source heat pumps retrofitted to radiator heating systems and measures to reduce space heating temperatures in existing buildings," Energy, Elsevier, vol. 242(C).
    13. Lu, Yuehong & Wang, Shengwei & Yan, Chengchu & Huang, Zhijia, 2017. "Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties," Applied Energy, Elsevier, vol. 187(C), pages 62-71.
    14. Wu, Raphael & Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2017. "Multiobjective optimisation of energy systems and building envelope retrofit in a residential community," Applied Energy, Elsevier, vol. 190(C), pages 634-649.
    15. Brinks, Pascal & Kornadt, Oliver & Oly, René, 2016. "Development of concepts for cost-optimal nearly zero-energy buildings for the industrial steel building sector," Applied Energy, Elsevier, vol. 173(C), pages 343-354.
    16. Østergaard, Dorte Skaarup & Tunzi, Michele & Svendsen, Svend, 2021. "What does a well-functioning heating system look like? Investigation of ten Danish buildings that utilize district heating efficiently," Energy, Elsevier, vol. 227(C).
    17. Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).

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