Development of a digital twin of a network of heating systems for smart cities on the example of the city of Almaty

Авторы:

Баяндина Гульмира Сапаровна (Соавтор)

ISSN

2088-8708

Область исследования

Инжиниринг и технологии

Тип исследования

Прикладное

Аннотация (краткое описание)

Abstract In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.

Соответствие целям устойчивого развития (ЦУР)

7. 

Недорогая и чистая энергия

9. 

Индустриализация, инновации и инфраструктура

13. 

Борьба с изменением климата

URI

https://www.scopus.com/sourceid/21100373959

DOI

10.11591/ijece.v14i6.pp6656-6674

Практическое значение

In conclusion, the creation of a digital twin for the heating network of smart cities, along with a mathematical model and big data analysis, has great potential for improving development planning and improving the efficiency and stability of urban heating systems. To overcome the difficulties that smart cities face in regulating their heating networks, the initial study was aimed at exploring a combination of digital twin technologies, mathematical modeling, and big data analysis. A review of the literature revealed the importance of heating systems in smart cities, as well as the function of digital twin technology in creating a virtual version of the real infrastructure. Thanks to real-time monitoring, analysis and forecasting, which are made possible by the digital twin, it is possible to better distribute and optimize the operation of heating networks.

Квартиль

Q2

Источник квартиля

SCImago (Scopus)

Данный документ располагается в коллекциях

Международные публикации ученых SU

Scopus

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