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Simulation of Power Consumption by Household Consumers Taking Into Account the Geographic Location and Meteorological Conditions of the Republic of Tajikistan1

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Power Technology and Engineering Aims and scope

The article proposes simulation of power consumption, determining the dependence of the geographic location above sea level and meteorological conditions of the location of household consumers in the Republic of Tajikistan. Mathematical and computer models, which allow taking into account the influence of being above sea level and of meteorological conditions at the location of household consumers on power consumption, have been proposed. Comparisons of the results obtained in the models with the results of experimental data taken from the readings of power metering devices showed high convergence.

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References

  1. A. I. Sidorov and S. Sh. Tabarov, “Rationing of power consumption of the Republic of Tajikistan taking into account the climatic features of the region,” Énerget. Edinoi Seti, No. 3(5), 70 – 75 (2019).

  2. S. Sh. Tabarov, “Specific power consumption of the household sector, taking into account the ambient temperature and the territorial location of the Republic of Tajikistan,” Prom. Énerget., No. 7, 19 – 22 (2019).

  3. A. I. Sidorov, O. A. Khanzhina, S. Sh. Tavarov, and M. S. Osimi, “Ensuring the efficiency of distribution networks C. Dushanbe and Republic of Tajikistan,” in: Int. Multi-Conf. on Industrial Engineering and Modern Technologies (FarEastCon-2019), pp. 1 – 4 (2019).

  4. A. I. Sidorov, S. Sh. Tabarov, and G. Kh. Madzhidov, “Reliability indexes of components of the 6 – 10 kV distribution network,” Vopr. Élektrotekhnol., No. 3(24), 55 – 61 (2019).

  5. A. I. Sidorov and S. Sh. Tavarov, “Method for forecasting electric consumption for household users in the conditions of the Republic of Tajikistan,” Int. J. Sust. Dev. Plan., 15(4), 569 – 574 (2020).

    Google Scholar 

  6. S. Sh. Tavarov, “Method for predicting power consumption by household consumers in the conditions of the Republic of Tajikistan,” Vestn. YuUrGU Ser. Énerget., 20(2), 28 – 35 (2020).

    Google Scholar 

  7. A. I. Sidorov and S. Sh. Tavarov, “Enhancing reliability of electricity supply of city electric networks cities of Dushanbe,” B. Electr. Eng. Inf., 10(1), 46 – 54 (2020)

    Google Scholar 

  8. S. Huang, H. Cheng, Z. D. Li., H. Z. Zhang, J. L. Li, and J. Y. Guo, “A novel invulnerability index for invulnerability assessment of complex power network,” Eur. J. Electr. Eng., 21(1), 11 – 17 (2019).

    Article  Google Scholar 

  9. K. V. Blazakis, T. N. Kapetanakis, and G. S. Stavrakakis, “Effective electricity theft detection in power distribution grids using an adaptive neuro fuzzy inference system,” Energies, 13(12), 3110 (2020).

  10. H. J. Jabir, J. Teh, D. Ishak, and H. Abunima, “Impacts of demand-side management on electrical power systems: Areview,” Energies, 11(5), 1050 (2018).

  11. F. Issi and O. Kaplan, “The determination of load profiles and power consumptions of home appliances,” Energies, 11(3), 607 (2018).

  12. L. Angrisani, F. Bonavolonta, A. Liccardo, R. Schiano Lo Moriello, and F. Serino, “Smart power meters in augmented reality environment for electricity consumption awareness,” Energies, 11(9), 2303 (2018).

  13. K. Yan, X. Wang, Y. Du, N. Jin, H. Huang, and H. Zhou, “Multi-step short-term power consumption forecasting with a hybrid deep learning strategy,” Energies, 11(11), 3089 (2018).

  14. S. Vojtovic, A. Stundziene, and R. Kontautiene, “The impact of socio-economic indicators on sustainable consumption of domestic electricity in Lithuania,” Sustainability, 10(2), 162 (2018).

  15. F. Ziel, “Load nowcasting: predicting actuals with limited data,” Energies, 13(6), 1443 (2020).

    Article  Google Scholar 

  16. A. Naz, M. U. Javed, N. Javaid, T. Saba, M. Alhussein, and K. Aurangzeb, “Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids,” Energies, 12(5), 866 (2019).

  17. W. M. To, P. K. C. Lee, and T. M. Lai, “Modeling of monthly residential and commercial electricity consumption using nonlinear seasonal models — the case of Hong Kong,” Energies, 10(7), 885 (2017).

  18. S. Ryu, J. Noh, and H. Kim, “Deep neural network based demand side short term load forecasting,” Energies, 10(1), 3 (2017).

  19. B. I. Makoklyuev, “Tendencies in power consumption of energy systems in Russia,” Énerget. Edinoi Seti, No. 5(48), 56 – 64 (2019).

  20. B. I. Makoklyuev, A. S. Polizharov, A. A. Basov, Yu. E. Alla, and S. V. Loktionov, “Short-term forecasting of power consumption in power systems,” Élektr. Stantsii, No. 4, 24 – 35 (2018).

    Google Scholar 

  21. B. I. Makoklyuev, A. S. Polizharov, A. V. Antonov, M. N. Govorun, A. V. Kolesnikov, A. A. Basov, and Yu. E. Alla, “Operative correction of the power consumption schedules in the planning cycle of the balancing market,” Élektr. Stantsii, No. 5, 36 – 44 (2019).

  22. N. G. Repkina, “Studying the factors affecting the accuracy of forecasting daily power consumption,” Izv. Vuzov Élektromekh., No. 2, 41 – 43 (2015).

  23. V. A. Zubakin and N. M. Kovshov, “Methods and models for analyzing the volatility of power consumption taking into account cyclicality and stochasticity,” Strateg. Biznesa, No. 7(15), 6 – 12 (2015).

  24. G. S. Valeev, M. A. Dzyuba, and R. G. Valeev, “Modeling daily load curves of the elements of 6 – 10 kV urben power distribution networks under conditions of limited input information,” Vestn. YuUrGU Ser. Énerget., 16(2), 23 – 29 (2016).

    Google Scholar 

  25. A. M. Abdurahmanov, M. V. Volodin, E. Yu. Zybin, and V. N. Ryabchenko, “Forecasting methods in electricity distribution networks (review),” Russ. Internet J. Electr. Eng., 3(1), 3 – 23 (2016).

    Article  Google Scholar 

  26. I. A. Solov’eva and A. P. Dzyuba, “Forecasting electric energy consumption with factors of process and market environment taken into account,” Nauchn. Dialog, No. 7 (19), 97 – 113 (2013).

  27. SP 256.1325800.2016. Electrical Equipment of Residential and Public Buildings. Rules for Design and Erection [in Russian], AO Kodeks, St. Petersburg (2017).

  28. RM-2696–01. Temporary Instructions for Calculating Electrical Loads for Residential Buildings [in Russian], GUP NIATs, Moscow (2001).

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Authors and Affiliations

  1. South Ural State University, Chelyabinsk, Russia

    S. Sh. Tabarov

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  1. S. Sh. Tabarov
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Correspondence to S. Sh. Tabarov.

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As a matter for discussion (from Editorial Board).

Translated from Élektricheskie Stantsii, No. 1, January 2021, pp. 45 – 49. DOI: 10.34831/EP.2021.1074.1.006

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Tabarov, S.S. Simulation of Power Consumption by Household Consumers Taking Into Account the Geographic Location and Meteorological Conditions of the Republic of Tajikistan1. Power Technol Eng 55, 305–309 (2021). https://doi.org/10.1007/s10749-021-01356-6

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  • DOI: https://doi.org/10.1007/s10749-021-01356-6

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