Smart City Gnosys

Smart city article details

Title	Development Of A Digital Twin Of A Network Of Heating Systems For Smart Cities On The Example Of The City Of Almaty
ID_Doc	19567
Authors	Tyulepberdinova G.; Kunelbayev M.; Shiryayeva O.; Sakypbekova M.; Sarsenbayev N.; Bayandina G.
Year	2024
Published	International Journal of Electrical and Computer Engineering, 14, 6
DOI	http://dx.doi.org/10.11591/ijece.v14i6.pp6656-6674
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. © 2024 Institute of Advanced Engineering and Science. All rights reserved.
Author Keywords	Digital twin; Heating system; K-neighbor regression; Linear regression; Machine learning; Random forest regression; Smart city

Similar Articles

Id	Similarity	Authors	Title	Published
9933	0.913	Omirgaliyev R.; Zhakiyev N.; Aitbayeva N.; Akhmetbekov Y.	Application Of Machine Learning Methods For The Analysis Of Heat Energy Consumption By Zones With A Change In Outdoor Temperature: Case Study For Nur-Sultan City	International Journal of Sustainable Development and Planning, 17, 4 (2022)
57240	0.898	Shafiq M.; Bhavani N.P.G.; Venkata Naga Ramesh J.; Veeresha R.K.; Talasila V.; Sulaiman Alfurhood B.	Thermal Modeling And Machine Learning For Optimizing Heat Transfer In Smart City Infrastructure Balancing Energy Efficiency And Climate Impact	Thermal Science and Engineering Progress, 54 (2024)
36050	0.882	Samikwa E.; Schärer J.; Braun T.; Di Maio A.	Machine Learning-Based Energy Optimisation In Smart City Internet Of Things	Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2023)
58100	0.878	Testasecca T.; Lazzaro M.; Sirchia A.	Towards Digital Twins Of Buildings And Smart Energy Networks: Current And Future Trends	2023 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2023 - Proceedings (2023)
35996	0.877	Haque A.; Malik A.	Machine Learning In Renewable Energy Systems For Smart Cities	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things (2024)
22431	0.873	Rajaan R.; Baishya B.K.; Rao T.V.; Pattanaik B.; Tripathi M.A.; Anitha R.	Efficient Usage Of Energy Infrastructure In Smart City Using Machine Learning	EAI Endorsed Transactions on Internet of Things, 10 (2024)
5629	0.872	Gonzalez-Vidal A.; Mendoza-Bernal J.; Niu S.; Skarmeta A.F.; Song H.	A Transfer Learning Framework For Predictive Energy-Related Scenarios In Smart Buildings	IEEE Transactions on Industry Applications, 59, 1 (2023)
35924	0.872	Deepica S.; Kalavathi S.; Angelin Blessy J.; Vianny D.M.M.	Machine Learning Based Approach For Energy Management In The Smart City Revolution	Hybrid Intelligent Approaches for Smart Energy: Practical Applications (2022)
49207	0.869	Gao Q.; Chu Y.; Peng Z.; Jin Y.; Ji X.; Ji S.; Ping S.; Xie X.; Zhu X.; Yue Y.	Smart Building Management System Based On Digital Twin: A Case Study On Real-Time Environmental Monitoring And Thermal Comfort Prediction	Proceedings - 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024 (2024)
762	0.869	Wu J.; Nguyen S.; Alahakoon D.; De Silva D.; Mills N.; Rathnayaka P.; Moraliyage H.; Jennings A.	A Comparative Analysis Of Machine Learning-Based Energy Baseline Models Across Multiple Building Types	Energies, 17, 6 (2024)