The Thermal Regime Simulation and the Heat Management of a Smart Building

Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy


Release:

2018, Vol. 4. №2

Title: 
The Thermal Regime Simulation and the Heat Management of a Smart Building


For citation: Zakharov A. A., Zakharova I. G., Romazanov A. R., Shirokikh A. V. 2018. “The Thermal Regime Simulation and the Heat Management of a Smart Building”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 4, no 2, pp. 105-119. DOI: 10.21684/2411-7978-2018-4-2-105-119

About the authors:

Alexander A. Zakharov, Dr. Sci (Tech.), Professor, Secure Smart City Information Technologies Department, University of Tyumen; a.a.zakharov@utmn.ru

Irina G. Zaharova, Cand. Sci. (Phys.-Math.), Professor, Department of Software, University of Tyumen; i.g.zakharova@utmn.ru

Artur R. Romazanov, Master Student, Software Department, University of Tyumen; qwsr11@gmail.com

Andrey V. Shirokikh, Cand. Sci. (Tech.), Professor, Secure Smart City Information Technologies Department, University of Tyumen; a.v.shirokih@utmn.ru

Abstract:

High heating costs and insufficient efficiency of the large buildings temperature control determine the need to develop innovative methods for the heat supply optimization, taking advantage of the Smart Building technologies. This article suggests an approach to automaticize the heat supply management in a smart building. The goal is to reduce financial costs while maintaining a comfortable thermal regime. The proposed approach uses the possibility of obtaining detailed information from temperature sensors, its interpretation, monitoring of changes, and the generation of adequate heat management solutions in real time. The authors propose an object-oriented model of the building’s temperature regime, which includes the description of the object attributes (individual room), geometric connections and physical boundary conditions between these objects. Real floor plans of the building determine the geometric relationships. Physical boundary conditions come from the heat transfer model.

The authors show the possibilities of machine learning methods for clarifying the values of the parameters that determine the heat transfer processes in the building. In addition, they suggest methods for rooms classification from the position of the temperature regime characteristic for each room under various external conditions. The proposed approach forms the basis of an information system for modeling the thermal regime and controlling the heat supply of the building. The system provides both simulations based on the generated temperature data and computational experiments with real data periodically requested from sensors installed inside and outside the building. Machine learning modules provide a permanent adjustment of the building model in the process of obtaining new information about real temperature conditions.

References:

  1. Bashmakov I. A. 2008. “Analiz osnovnyh tendencij razvitiya sistem teplosnabzheniya v Rossii i za rubezhom” [Analysis of the Main Trends in the Heat Supply Systems Development in Russia and Abroad]. Novosti teplosnabzheniya, no 2, pp. 6-10.
  2. Bashmakov I. A. 2015. “Energoeffektivnost' zdaniy v Rossii i v zarubezhnyh stranah” [Energy Efficiency of the Buildings in Russia and in the Foreign Countries]. Energosberezhenie, no 3, pp. 24-29.
  3. Bashmakov I. A. 2015. “Energopotreblenie zdaniy sfery uslug: mirovoy opyt” [Energy Consumption of the Service Buildings: the World Experience]. Energosberezhenie, no 5, pp. 24-29.
  4. Koval'nogov N. N., Rtishcheva A. S., Tsynaeva E. A. 2007. “Avtomatizirovannaya sistema optimal'nogo upravleniya otopleniem uchebnogo zavedeniya” [Automated System of the Optimal Heating Management in an Educational Institution]. Izvestiya vysshih uchebnyh zavedeniy. Problemy energetiki, no 3-4, pp. 100-107.
  5. Koshel S. M., Musin O. R. 2000. “Metody tsifrovogo modelirovaniya: kriging i radial'naya interpolyatsiya” [Methods of the Digital Modeling: Kriging and Radial Interpolation]. Informatsionnyy byulleten' GIS-Assotsiatsii, no 4, pp. 26-30.
  6. Matyas A. Yu. 2009. “Algoritmicheskoe obespechenie sistem temperaturnogo kontrolya” [Algorithmic Support of the Temperature Control Systems]. Polzunovskiy al'manah, no 2, pp. 11-15.
  7. Moiseykina L. G., Darda E. S. 2017. “Analiz strukturnyh izmeneniy vnutrennego potrebleniya TER g. Moskvy” [Analysis of Structural Changes in the Domestic Consumption of the Energy Resources in Moscow]. Statistika i ekonomika, no 6, pp. 22-31.
  8. Pugovkin A.V., Kuprekov S. V., Abushkin D. V., Zarechnaya I. A., Muslimova N. I. 2010. “Matematicheskaya model' teplosnabzheniya pomeshcheniy dlya ASU energosberezheniya” [Mathematical Model of the Heat Supply for Automated Power Management Systems]. Doklady TUSUR. Upravlenie, vychislitel'naya tekhnika i informatika, no 2 (22), p. 1, pp. 293—298.
  9. Suchkova L. I., Yakimenko T. V., Homutov O. I. 2007. “Intellektual'nye kontrollery sistem temperaturnogo kontrolya i regulirovaniya” [Intelligent Controllers of Temperature Control and Regulation Systems]. Polzunovskiy al'manah, no 3, pp. 96-98. 
  10. RF Federal law no 261-FZ "Ob energosberezhenii i o povyshenii energeticheskoy effektivnosti i o vnesenii izmeneniy v otdel'nye zakonodatel'nye akty Rossiyskoy Federatsii" [On Energy Saving and on Improving Energy Efficiency and on Amending Certain Legislative Acts of the Russian Federation]. Accessed on April 18, 2018. http://base.consultant.ru/cons/cgi/online.cgi?req=doc;base=LAW;n=93978
  11. Frumkin A. M., Gromova E. N., Yatsevich V. A. 2016. "Uproshchennaya imitatsionnaya model' otaplivaemogo pomeshcheniya kak ob"ekta upravleniya" [Simplified Simulation Model of a Heated Room as a Control Object]. Auditorium, no 2 (10) pp. 93-103.
  12. Anvari-Moghaddam A., Monsef H., Rahimi-Kian A. 2015. “Cost-effective and Comfort-aware Residential Energy Management under Different Pricing Schemes and Weather Conditions”. Energy and Buildings, vol. 86, pp. 782-793. DOI: 10.1016/j.enbuild.2014.10.017
  13. Ascione F., Bianco N., De Stasio C., Mauro G. M., Vanoli G. P. 2016. “Simulation-based Model Predictive Control by the Multi-objective Optimization of Building Energy Performance and Thermal Comfort”. Energy and Buildings, vol. 111, pp. 131-144. DOI: 10.1016/j.enbuild.2015.11.033
  14. Figueiredo J., da Costa J. S. 2012. “A SCADA System for Energy Management in Intelligent Buildings”. Energy and Buildings, vol. 49, pp. 85-98. DOI: 10.1016/j.enbuild.2012.01.041
  15. FIWARE: A Standard Open Platform for Smart Cities. Accessed on April 18, 2018. https://www.fiware.org/2015/03/25/fiware-a-standard-open-platform-for-smart-cities.
  16. Gwerder M., Gyalistras D., Oldewurtel F., Lehmann B., Wirth K., Stauch V., Tödtli C. J. 2010. “Potential Assessment of Rule-based Control for Integrated Room Automation”. Proceedings of the 10th REHVA World Congress — Clima 2010 (May 9-12, 2010, Turkey, Antalya), pp. 9-12.
  17. Ock J., Issa R. R. A., Flood I. 2016. “Smart Building Energy Management Systems (BEMS) Simulation Conceptual Framework. 2016. Proceedings of the 2016 Winter Simulation Conference (December 11-14, 2016, USA, VA, Arlington), pp. 3237-3245. IEEE Press. DOI: 10.1109/WSC.2016.7822355
  18. Privara S., Cigler J., Váňa Z., Oldewurtel F., Sagerschnig C., Žáčeková E. 2013. “Building Modeling as a Crucial Part for Building Predictive Control”. Energy and Buildings, vol. 56, pp. 8-22. DOI: 10.1016/j.enbuild.2012.10.024
  19. Senave M., Reynders G., Verbeke S., Saelens D. 2017. “A Simulation Exercise to Improve Building Energy Performance Characterization via On-board Monitoring”. Energy Procedia, vol. 132, pp. 969-974. DOI: 10.1016/j.egypro.2017.09.687
  20. Tashtoush B., Molhim M., Al-Rousan M. 2005. “Dynamic Model of an HVAC System for Control Analysis”. Energy, vol. 30, no 10, pp. 1729-1745. DOI: 10.1016/j.energy.2004.10.004