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

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. Zakharova, Cand. Sci. (Phys.-Math.), Professor, Department of Software, School of Computer Science, University of Tyumen, Tyumen, Russia; i.g.zakharova@utmn.ru, https://orcid.org/0000-0002-4211-7675

Artur R. Romazanov, Teaching Assistant, Software Department, University of Tyumen; a.r.romazanov@utmn.ru

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.

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