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A critical appraisal of energy-signature models

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  • Hammarsten, Stig

Abstract

The use of different energy-signature (ES) models for energy consumption predictions and building parameter estimations is reviewed. For predictions using time-steps of one day or longer, static ES models are found to be useful. Recommendations for the choice of model are given. The use of ES models for the estimation of building parameters, e.g. for an energy audit, should only be done with great caution, as there can be considerable errors. The development of more sophisticated dynamic models may solve some of the problems encountered with the static models discussed here.

Suggested Citation

  • Hammarsten, Stig, 1987. "A critical appraisal of energy-signature models," Applied Energy, Elsevier, vol. 26(2), pages 97-110.
    • Handle: RePEc:eee:appene:v:26:y:1987:i:2:p:97-110
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    1. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    3. Nikolaos Barmparesos & Dimitra Papadaki & Michalis Karalis & Kyriaki Fameliari & Margarita Niki Assimakopoulos, 2019. "In Situ Measurements of Energy Consumption and Indoor Environmental Quality of a Pre-Retrofitted Student Dormitory in Athens," Energies, MDPI, vol. 12(11), pages 1-19, June.
    4. Ingrid Allard & Thomas Olofsson & Gireesh Nair, 2017. "Energy Performance Indicators in the Swedish Building Procurement Process," Sustainability, MDPI, vol. 9(10), pages 1-23, October.
    5. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
    6. Henchoz, Samuel & Weber, Céline & Maréchal, François & Favrat, Daniel, 2015. "Performance and profitability perspectives of a CO2 based district energy network in Geneva's City Centre," Energy, Elsevier, vol. 85(C), pages 221-235.
    7. Christoffer Rasmussen & Peder Bacher & Davide Calì & Henrik Aalborg Nielsen & Henrik Madsen, 2020. "Method for Scalable and Automatised Thermal Building Performance Documentation and Screening," Energies, MDPI, vol. 13(15), pages 1-23, July.
    8. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    9. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
    10. Christoffer Rasmussen & Niels Lassen & Peder Bacher & Tor Helge Dokka & Henrik Madsen, 2023. "Data-Driven Estimation of Time-Varying Stochastic Effects on Building Heat Consumption Related to Human Interactions," Energies, MDPI, vol. 16(16), pages 1-22, August.
    11. Olofsson, Thomas & Mahlia, T.M.I., 2012. "Modeling and simulation of the energy use in an occupied residential building in cold climate," Applied Energy, Elsevier, vol. 91(1), pages 432-438.
    12. Allard, I. & Olofsson, T. & Hassan, O.A.B., 2013. "Methods for energy analysis of residential buildings in Nordic countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 306-318.
    13. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    14. Palmer Real, Jaume & Møller, Jan Kloppenborg & Li, Rongling & Madsen, Henrik, 2022. "A data-driven framework for characterising building archetypes: A mixed effects modelling approach," Energy, Elsevier, vol. 254(PB).

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