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Classification of heating conditions in terms of smart control of indoor heating with the use of uncontrolled electric heaters

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Category: Content №4 2022

Last Updated on 29 August 2022

Published on 30 November -0001

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Authors:

G.Pivnyak, orcid.org/0000-0002-8462-2995, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

G.Gruhler, orcid.org/0000-0002-3624-5259, Reutlingen University, Reutlingen, the Federal Republic of Germany, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.Bublikov, orcid.org/0000-0003-3015-6754, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Yu.Papaika, orcid.org/0000-0001-6953-1705, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ye.Voskoboinyk, orcid.org/0000-0003-1178-6486, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (4): 078 - 083

https://doi.org/10.33271/nvngu/2022-4/078

Abstract:

Purpose. To reduce specific energy consumption for heating municipal and industrial buildings by introducing smart indoor temperature control taking into consideration individual dependences of characteristics of each person as a consumer of energy resources on specific heating conditions.

Methodology. The energy-efficient and smart control of indoor heating is based on the fact that a control system is to elaborate and provide a compromise solution as for comfortable perception of proper conditions of someones staying indoors and minimum consumption of energy resources. To do that, first of all the problem should be solved concerning recognition of different heating conditions by a smart control system aimed at providing a process of system learning and database formation. To complete this task, the parameters of one-dimensional dynamic models describing heat-exchange processes are proposed to be used as the information signs for the classification of situations in terms of heating relative to the uncontrolled electric heaters; the input value is the heater capacity, and the output value is the air temperature within the local indoor zone. Within the framework of the development of a method for classifying indoor heating conditions, dependences of the parameters of dynamic models of local indoor heating zones on the characteristics of local heating zones were analysed. Besides, certain regularities of a control process for heaters were determined; that helped provide accurate identification of the models of local heating zones without considerable changes in a preset temperature mode. Computational experiments made it possible to evaluate the accuracy of determination of information signs for the classification of heating conditions while representing real characteristics of indoor heat-exchange processes.

Findings. The studies resulted in the development of a method for identifying dynamic properties of indoor heat zones for the cases of using uncontrolled electric heaters with two states.

Originality. For the first time, certain regularities have been identified concerning a capacity control process for electric heaters with two states and a process of temperature measurement within the local indoor zones. The regularities made it possible to determine the parameters of dynamic models of indoor heat-exchange processes with high accuracy and without considerable changes in the preset temperature mode, and to use these parameters as information signs while classifying the heating conditions.

Practical value. The obtained regularities of the processes of heater control and temperature measurement allowed developing a method for identification of dynamic properties of local indoor heat zones, which makes up the basis for a classification procedure of heating conditions.

Keywords: energy-efficient heating of buildings, smart control, classification of heating conditions

References.

1. Matveieva, Yu.., Kolosok, S.., & Vakulenko, .. (2019). Analysis of the world practices to provide energy efficiency based upon smart grid model. Efficient Economy, 4. https://doi.org/10.32702/2307-2105-2019.4.36.

2.Derii, V.. (2021). Trends in the development of the district heating systems of Ukraine. Collection of scientific papers Problemy zahalnoi enerhetyky, 1(64), 52-59. https://doi.org/10.15407/pge2021.01.052.

3. Spodyniuk, N.., & Shepitchiak, V.B. (2020). Cost analysis of thermal energy, and ways to economize it for residential buildings. Scientific messenger of NFTU of Ukraine, 30(2), 62-65. https://doi.org/10.36930/40300211.

4. Hargreaves, J.J., & Jones, R.A. (2020). Long Term Energy Storage in Highly Renewable Systems. Frontiers in Energy Research, 8. https://doi.org/10.3389/fenrg.2020.00219.

5. Stolojescu-Crisan, C., Crisan, C., & Butunoi, B.P. (2021). An IoT-Based Smart Home Automation System. Sensors (Basel, Switzerland), 21(11), 3784. https://doi.org/10.3390/s21113784.

6. Pau, G., Collotta, M., Ruano, A., & Qin, J. (2017). Smart Home Energy Management. Energies, 10, 382. https://doi.org/10.3390/en10030382.

7. Yu, Y., Yang, J., & Chen, B. (2012). The Smart Grids in China AReview. Energies, 5, 1321-1338. https://doi.org/10.3390/en5051321.

8. Barbato, A., Capone, A., Chen, L., Martignon, F., & Paris, S. (2015). A Distributed Demand-Side Management Framework for the Smart Grid. Computer Communications, 57, 13-24. https://doi.org/10.1016/j.comcom.2014.11.001.

9. Perera, Degurunnehalage W.U., & Skeie, Nils-Olav (2017). Comparison of Space Heating Energy Consumption of Residential Buildings Based on Traditional and Model-Based Techniques. Buildings, 7(2), 27. https://doi.org/10.3390/buildings7020027.

10. Gitakarma, M.S., & Priyambodo, T.K. (2019). A Real-Time Smart Home System using Android Bluetooth Control Device Module. 2019 International Symposium on Electronics and Smart Devices (ISESD), 1-7. https://doi.org/10.1109/isesd.2019.8909522.

11. Zaslavsky, A.M., Tkachov, V.V., Protsenko, S.M., Bublikov,A.V., Suleimenov, B., Orshubekov, N., & Gromaszek, K. (2017). Self-organizing intelligent network of smart electrical heating devices as an alternative to traditional ways of heating. Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017. https://doi.org/10.1117/12.2281225.

12. Tkachov, V., Gruhler, G., Zaslavski, A., Bublikov, A., & Protsenko, S. (2018). Development of the algorithm for the automated synchronization of energy consumption by electric heaters under condition of limited energy resource. Eastern-European Journal of Enterprise Technologies, 2(8-92), 50-61. https://doi.org/10.15587/1729-4061.2018.126949.

13. Machorro Cano, I., Alor-Hernndez, G., Paredes-Valverde, M., Rodrguez, L., Snchez-Cervantes, J., & Olmedo-Aguirre, J. (2020). HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving. Energies, 13. https://doi.org/10.3390/en13051097.

14. Ma, Yunlong, Chen, Xiao, Wang, Liming, & Yang, Jianlan (2021). Study on Smart Home Energy Management System Based on Artificial Intelligence. Journal of Sensors, 1-9. https://doi.org/10.1155/2021/9101453.

15. Bublikov, .V., Boiko, .., Voskoboinik, Ye.., Slavinskii,D.V. & Shevchenko, V.. (2021) Developing a model of discrete system for the automated control of group of heaters in the context electrical heating. Collection of scientific papers of the National Mining University, 66-21, 233-244. https://doi.org/10.33271/crpnmu/66.233.

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