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Modelowanie matematyczne i system sterowania budynkiem niemal zeroenergetycznym
Języki publikacji
Abstrakty
The article examines three different kinds of mathematical model of nearly zero energy building. The first model enables to optimize the structure and the definition of key parameters of energy efficient building. The second model is necessary for passive house designing with renewable energy sources. The third model should be used for monitoring and control of energy supply system of nearly zero energy building through year every hour of winter and summer.
W artykule rozważono trzy różne typy matematycznego modelu budynku o niemal zerowym zużyciu energii. Pierwszy model pozwala zoptymalizować strukturę i definicję kluczowych parametrów budynku energooszczędnego. Drugi model jest niezbędny do zaprojektowania domu pasywnego wykorzystującego odnawialne źródła energii. Trzeci model powinien być stosowany do godzinowego monitorowania i monitorowania systemu energetycznego budynku o niskim zużyciu energii przez cały rok.
Rocznik
Tom
Strony
21--24
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
- Department of Industrial Thermal Engineering Systems, National Research University “MPEI”
autor
- Institute for Information Technology, Technische Universität Ilmenau
autor
- Department of Industrial Thermal Engineering Systems, National Research University “MPEI”
autor
- Department of Industrial Thermal Engineering Systems, National Research University “MPEI”
autor
- Department of Industrial Thermal Engineering Systems, National Research University “MPEI”
autor
- Department of Industrial Thermal Engineering Systems, National Research University “MPEI”
autor
- Department of Industrial Thermal Engineering Systems, National Research University “MPEI”
Bibliografia
- [1] Bacher P., Madsen H., Nielsen H.A., Perers B.: Short-term heat load forecasting for single family houses. Energy and Buildings 38/2006, 63–71.
- [2] Bacher P., Madsen H., Nielsen H.A.: Online short-term solar power forecasting. Solar Energy 83/2009, 1772–1783.
- [3] Cemesova A. et al.: PassivBIM: Enhancing interoperability between BIM and low energy design software. Automation in Construction 57/2015, 17–32.
- [4] Fabrizio E. et al.: A model to design and optimize multi-energy systems in buildings at the design concept stage. Renewable Energy 35/2010, 644–655.
- [5] Fan Ch. et al.: Temporal knowledge discovery in big BAS data for building energy management. Energy and Buildings 109/2015, 75–89.
- [6] Feist V.: Die Hauptlagen nach der Projektierung der passiven Häuser. Konti Print, Moscow 2015.
- [7] Halvgaard R. et al.: Model predictive control for a smart solar tank based on weather and consumption forecasts. Energy Procedia 30/2012, 270–278.
- [8] Michailidis I.T. et al.: Proactive control for solar energy exploitation: A german high-inertia building case study. Applied Energy 155/2015, 409–420.
- [9] Oti A.H. et al.: A framework for the utilization of Building Management System data in building information models for building design and operation. Automation in Construction 72/2016, 195–210.
- [10] Sultanguzin I., Kalyakin I., Govorin A. et.al.: Optimization of the energy efficient active house. 3 Ingenieurtag 2016. Neseff-Netzwerktreffen 2016. Tagungsband. Branden-burgische Technische Universität. Cottbus-Senftenberg. 14-15 November 2016, 8–12.
- [11] Sultanguzin I.A., Isaev M.V., Kurzanov S.Yu.: Optimizing the production of coke, coal chemicals, and steel on the basis of environmental and energy criteria. Metallurgist 54/2011, 600–607.
- [12] Toepfer H., Goetze M., Chervakova E., Hutschenreuther T.: On the Use of Wireless Sensors Within a Traffic Monitoring System. Proceedings of the International Academic Forum AMO–SPITSE–NESEFF. Smolensk Publishing “Universum”, Moscow–Smolensk 2016.
- [13] Torunski E. et al.: A review of smart environments for energy savings. Procedia Computer Science 10/2012, 205–214.
- [14] Wang Y., Kuckelkorn J., Liu Y.: State of art review on methodologies for control strategies in low energy buildings in the period from 2006 to 2016. Energy & Buildings 147/2017, 27–40.
- [15] Zhou K. et al.: Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews 56/2016, 215–225.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b65b3aec-2a74-4b96-aea3-6ed1634a6d40