Calibration of a High-Resolution Dynamic Model for Detailed Investigation of the Energy Flexibility of a Zero Emission Residential Building

  • John ClaußEmail author
  • Pierre Vogler-Finck
  • Laurent Georges
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)


A detailed multi-zone building model of an existing zero emission residential building (ZEB) has been created using the software IDA Indoor Climate and Energy (IDA ICE). The model will later be used for investigating control strategies for the heating system to activate the building energy flexibility. The main purpose of this paper is to show how reliable the model reproduces the short-term thermal dynamics and the temperature zoning of the building. This is of particular interest for the control of heating, ventilation and air conditioning (HVAC) systems in order to provide meaningful insights of active demand response (ADR) measures. The model has been validated using data sets from seven experiments. Two dimensionless indicators, the normalized mean bias error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) were applied in order to evaluate the trend of the average indoor temperatures. The first approach considered standard operating conditions, where the measured indoor air temperature was used as input for the control of the electrical radiator and the total electricity use of the radiator as an output. Excitation sequences have been used in the second approach, where the electric power of the radiator has been imposed and the operative temperature taken as the output. The model shows good agreement between the temperature profiles from the measurements and the simulations based on the NMBE and CVRMSE remaining below 5% for most cases.


Detailed building model Model validation Zero emission building Energy flexibility Active demand response 



The authors would like to acknowledge IEA EBC Annex 67 “Energy Flexible Buildings” which was the platform of this collaboration. Access to the Living Laboratory was provided and funded by the Research Centre on Zero Emission Buildings project and its follow-up project Research Centre on Zero Emission Neighborhoods in Smart Cities. The PhD position of Pierre Vogler-Finck within the ADVANTAGE project is funded by the European Community´s 7th Framework Programme (FP7-PEOPLE-2013-ITN) under grant agreement no 607774.


  1. 1.
    E. Fabrizio, V. Monetti, Methodologies and advancements in the calibration of building energy models. Energies 8, 2548–2574 (2015)CrossRefGoogle Scholar
  2. 2.
    N. Zibin, R. Zmeureanu, J.A. Love, in A Bottom-up Method to Calibrate Building Energy Models Using Building Automation System (BAS) Trend Data. Aalborg Universitet CLIMA 2016—Proceedings of the 12th REHVA World Congress (2016)Google Scholar
  3. 3.
    F. Roberti, U.F. Oberegger, A. Gasparella, Calibrating historic building energy models to hourly indoor air and surface temperatures: methodology and case study. Energy Build. 108, 236–243 (2015)CrossRefGoogle Scholar
  4. 4.
    M. Taheri, F. Tahmasebi, A. Mahdavi, B. Ecology, in Two Case Studies in Optimization-Based Thermal Building Performance Model Calibration. Central European Symposium on Building Physics (2013)Google Scholar
  5. 5.
    P. Paliouras, N. Matzaflaras, R.H. Peuhkuri, J. Kolarik, Using measured indoor environment parameters for calibration of building simulation model—a passive house case study. Energy Procedia 78, 1227–1232 (2015)CrossRefGoogle Scholar
  6. 6.
    R. Simson, J. Kurnitski, K. Kuusk, Experimental validation of simulation and measurement-based overheating assessment approaches for residential buildings. Archit. Sci. Rev. 60, 192–204 (2017)CrossRefGoogle Scholar
  7. 7.
    F. Goia, L. Finocchiaro, A. Gustavsen, 7. Passivhus Norden | Sustainable Cities and Buildings The ZEB Living Laboratory at the Norwegian University of Science and Technology : a zero emission house for engineering and social science experiments (2015)Google Scholar
  8. 8.
    J. Clauß, I. Sartori, M.J. Alonso, M. Thalfeldt, L. Georges, in Investigations of Different Control Strategies for Heat Pump Systems in a Residential nZEB in the Nordic Climate. 12th IEA Heat Pump Conference 2017 (2017)Google Scholar
  9. 9.
    F. Kuznik, J. Virgone, Experimental investigation of wallboard containing phase change material: data for validation of numerical modeling. Energy Build. 41, 561–570 (2009)CrossRefGoogle Scholar
  10. 10.
    L. Georges et al., Evaluation of Simplified Space-Heating Hydronic Distribution for Norwegian Passive Houses (Trondheim, 2017)Google Scholar
  11. 11.
    EQUA, EQUA Simulation AB, 2015 [Online]. Available:
  12. 12.
    OpenStreetMap. Shiny weather data. [Online]. Available: Accessed on 20 May 2017
  13. 13.
    L. Lundström, in Mesoscale Climate Datasets for Building Modelling and Simulation. CLIMA 2016—Proceedings of the 12th REHVA World Congress (2016)Google Scholar
  14. 14.
    P. Bacher, H. Madsen, Identifying suitable models for the heat dynamics of buildings. Energy Build. 43(7), 1511–1522 (2011)CrossRefGoogle Scholar
  15. 15.
    H. Madsen et al., Thermal performance characterization using time series data—IEA EBC Annex 58 Guidelines (2015)Google Scholar
  16. 16.
    P. Vogler-Finck, J. Clauß, L. Georges, A dataset to support dynamical modelling of the thermal dynamics of a super-insulated building. Publication in progress (2017)Google Scholar
  17. 17.
    P. Schild, Personal communication (2017)Google Scholar
  18. 18.
    J. Granderson, S. Touzani, D. Jump, Assessment of Automated Measurement and Verification (M & V) Methods (2015)Google Scholar
  19. 19.
    ASHRAE, Measurement of energy and demand savings. ASHARE Guidel. 14-2002 8400, 1–165 (2002)Google Scholar
  20. 20.
    P. Vogler-Finck, J. Clauß, L. Georges, I. Sartori, R. Wisniewski, Inverse model identification of the thermal dynamics of a Norwegian zero emission house (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.Neogrid Technologies ApSAalborg ØDenmark

Personalised recommendations