Open Access
E3S Web Conf.
Volume 362, 2022
BuildSim Nordic 2022
Article Number 13006
Number of page(s) 9
Section Commissioning and Demand Response
Published online 01 December 2022
  1. Aanensen, T. and M. Holstad (2018). Tilgang og anvendelse av elektrisitet i perioden 1993-2017. Rapporter 2018/16, Statistisk sentralbyrå. [Google Scholar]
  2. ASHRAE (2014). Measurement of Energy Demand and Water Savings. Guideline 14. Atlanta, GA, USA, ASHRAE Standards Committee. [Google Scholar]
  3. Chong, A., et al. (2021). Calibrating building energy simulation models: A review of the basics to guide future work. Energy and Buildings 253: 111533. [CrossRef] [Google Scholar]
  4. Clauss, J., et al. (2018). Calibration of a high-resolution dynamic model for detailed investigation of the energy flexibility of a zero emission building. Proceedings from Cold Climate HVAC 2018. Kiruna. [Google Scholar]
  5. DiBK (2021). Klimabaserte energikrav til bygg -Høringssvar. D. f. byggkvalitet. Oslo, Direktoratet for byggkvalitet. 2022. [Google Scholar]
  6. Elhub (2021, 20.01.2021). Elhub and smart meters are enabling a more efficient Norwegian energy system. Retrieved 18.01, 2022 from [Google Scholar]
  7. Enova (2019). Forprosjekt Ny energimerkeordning -Hovedrapport. Enova SF. Trondheim. [Google Scholar]
  8. Fitton, R., et al. (2021). Building energy performance assessment based on in-situ measurements Challenges and general framework. Belgium, KU Leuven. [Google Scholar]
  9. Goia, F., et al. (2015). The ZEB Living Laboratory at the Norwegian University of Science and Technology: a zero emission house for engineering and social science experiments. Proceedings from 7th Passivhus Norden - Sustainable Cities and Buildings. Copenhagen. [Google Scholar]
  10. Grassi, B., et al. (2021). Dynamic Approach to Evaluate the Effect of Reducing District Heating Temperature on Indoor Thermal Comfort. Energies 14(1): 25. [Google Scholar]
  11. Lundström, L. (2018). Total solar irradiance according to ISO 52010-1:2017, GitHub. [Google Scholar]
  12. Lundström, L. (2019). Simulating space heating use -using the the ISO14N modelling framework, GitHub. [Google Scholar]
  13. Lundström, L. and J. Akander (2020). Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings. Energies 13(1): 76. [Google Scholar]
  14. Lundström, L., et al. (2019). Development of a Space Heating Model Suitable for the Automated Model Generation of Existing Multifamily Buildings—A Case Study in Nordic Climate. Energies 12(3): 485. [CrossRef] [Google Scholar]
  15. Manfren, M., et al. (2020). Open data and energy analytics - An analysis of essential information for energy system planning, design and operation. Energy 213: 118803. [CrossRef] [Google Scholar]
  16. OED (2021). Endr. i forskrift om endr. i forskrift om økonomisk og teknisk rapportering, inntektsramme for nettvirksomheten og tariffer. Oljeogenergidepartementet. [Google Scholar]
  17. Ruiz, G. R. and C. F. Bandera (2017). Validation of Calibrated Energy Models: Common Errors. Energies 10(10). [Google Scholar]
  18. Schoutsen, P. (2021). Energy Management in Home Assistant. Announcements. Retrieved 4. April 2022, 2022, from [Google Scholar]
  19. Skeie, K. (2020). Living Lab heating design evaluation, tehnical report, Trondheim, NTNU. [Google Scholar]
  20. Skeie, K. and A. Gustavsen (2021). Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling. Energies 14(4). [Google Scholar]
  21. van Dijk, D. (2019). EN ISO 52016-1: The New International Standard To Calculate Building Energy Needs for Heating And Cooling, Internal Temperatures And Heating And Cooling Load, Proceedings from Building Simulation 2019: 16th Conference of IBPSA, Rome. [Google Scholar]
  22. Vogler-Finck, P., Clauß, John, & Georges, Laurent (2017). A dataset to support dynamical modelling of the thermal dynamics of a super-insulated building. Zenodo. [Google Scholar]
  23. Yu, X., et al. (2019). Investigation of the Model Structure for Low-Order Grey-Box Modelling of Residential Buildings., Proceedings from Building Simulation 2019: 16th Conference of IBPSA, Rome. [Google Scholar]
  24. Yu, X., et al. (2022). Influence of data pre-processing and sensor dynamics on grey-box models for spaceheating: Analysis using field measurements. Building and Environment 212: 108832. [CrossRef] [Google Scholar]

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