Open Access
Issue
E3S Web Conf.
Volume 362, 2022
BuildSim Nordic 2022
Article Number 12003
Number of page(s) 6
Section Buildings and Flexibility
DOI https://doi.org/10.1051/e3sconf/202236212003
Published online 01 December 2022
  1. Andersen, K. H., S. K. Lien, H. T. Walnum, K. B. Lindberg, and I. Sartori (2021). Further development and validation of the ”PROFet” energy demand load profiles estimator. Building Simulation 2021 Conference, 1-3 Sep., Bruges, Belgium (BuildSim). [Google Scholar]
  2. Bacher, P. and H. Madsen (2011). Identifying suitable models for the heat dynamics of buildings. Energy & Buildings 43, 1511–1522. [CrossRef] [Google Scholar]
  3. Bagle, M. (2019). Investigation into the impact of thermal energy flexibility on cost optimal design and operation of Zero Emission Buildings. Master thesis, Department of Electric Power Engineering, NTNU. [Google Scholar]
  4. Bagle, M. E., P. Maree, H. T. Walnum, and I. Sartori (2021). Identifying grey-box models from archetypes of apartment block buildings. Building Simulation Conference. [Google Scholar]
  5. Bøeng, Ann Christin and Halvorsen, Bente and Larsen, Bodil M. (2014). “Oppvarming i boliger - Kartlegging av oppvarmingsutstyr og effektiviseringstiltak i hush-oldningene” Oppdragsrapport for NVE, NVE Report no. 84. [Google Scholar]
  6. Direktoratet for byggkvalitet (2016). Byggteknisk forskrift (TEK 10). [Google Scholar]
  7. Drgona, J., J. Arroyo, I. Cupeiro Figueroa, D. Blum, K. Arendt, D. Kim, E. P. Olle, J. Oravec, M. Wetter, D. L. Vrabie, and L. Helsen (2020). All you need to know about model predictive control for buildings. Annual Reviews in Control. [Google Scholar]
  8. Georges, L., E. Storlien, M. Askeland, and K. B. Lindberg (2021). Development of a data-driven model to characterize the heat storage of the building thermal mass in energy planning tools. E3S Web of Conferences 246, 10001. [CrossRef] [EDP Sciences] [Google Scholar]
  9. Hart, W. E., C. D. Laird, J.-P. Watson, D. L. Woodruff, G. A. Hackebeil, B. L. Nicholson, and J. D. Siirola (2017). Pyomo-optimization modeling in python (Second ed.), Volume 67. Springer Science & Business Media. [Google Scholar]
  10. Hedegaard, R., L. Friedrichsen, J. Tougaard, T. Mølbak, Steffen, and Petersen (2020). Usim2020-Building to Buildings: Urban and Community Energy Modelling, November 12th, 2020, Building Energy Flexibility as an asset in system-wide district heating optimization models. [Google Scholar]
  11. IEA EBC Annex 67 Energy Flexible Buildings (2019). Summary report - Energy in Buildings and Communities Programme Annex 67 Energy Flexible Buildings. [Google Scholar]
  12. Kristensen, N. R. and H. Madsen (2003). Continuous time stochastic modelling - Mathematics Guide. Technical University of Denmark. [Google Scholar]
  13. Lindberg, K. B., S. J. Bakker, and I. Sartori (2019). Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts. Utilities Policy 58, 63–88. [CrossRef] [Google Scholar]
  14. Lindberg, K. B., G. Doorman, D. Fischer, M. Korpas, A. Anestad, and I. Sartori (2016). Methodology for optimal energy system design of Zero Energy Buildings using mixed-integer linear programming. Energy and Buildings 127, 194–205. [CrossRef] [Google Scholar]
  15. NordPool (2021). Historical market data. https://www.nordpoolgroup.com/historical-market-data/. (Accessed on 03/18/2021). [Google Scholar]
  16. NorskKlimaservicesenter (2021). https://seklima.met.no/observations/. (Accessed on 03/18/2021). [Google Scholar]
  17. Romanchenko, D., J. Kensby, M. Odenberger, and F. Johnsson (2018). Thermal energy storage in district heating: Centralised storage vs. storage in thermal inertia of buildings. Energy Conversion and Management 162(February), 26–38. [CrossRef] [Google Scholar]
  18. Standards Norway (2020). Energy performance of buildings — Calculation of energy needs and energy supply (SN-NSPEK 3031:2020). [Google Scholar]
  19. Van Loan, C. (1978). Computing integrals involving the matrix exponential. IEEE Transactions on Automatic Control 23(3), 395–404. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.