Modeling household power consumption by residents of the Republic of Tajikistan

• Autorzy: Tavarov Saidjon Shiralievich , Smolin Anton Vyachislavovich , Safaraliev Murodbek Kholnazarovich , Kokin Sergey Evgenevich , Mirakov Ozod Abdulasanovich , Ahyoev Javod Salamshoevich

Identyfikatory

DOI

10.15199/48.2023.03.28

Warianty tytułu

PL

Modelowanie zużycia energii elektrycznej w gospodarstwach domowych przez mieszkańców Republiki Tadżykistanu

• Języki publikacji: EN

Abstrakty

EN

The paper proposes modeling power consumption, taking into account factors and identifying the dependence of the geographical location above sea level and meteorological conditions of the location of residential consumers of the Republic of Tajikistan on power consumption. A mathematical and computer model has been proposed, which makes it possible to take into account the influence of changes in geographic locations above sea level and meteorological conditions in the area where household consumers are located on power consumption. To compare the results obtained in the models, the results were compared with the results of experimental values; for the experimental data, the readings of the electricity metering devices were taken, which showed high convergence.

PL

W pracy zaproponowano modelowanie zużycia energii elektrycznej z uwzględnieniem czynników oraz określenie zależności położenia geograficznego nad poziomem morza oraz warunków meteorologicznych lokalizacji odbiorców mieszkaniowych Republiki Tadżykistanu od zużycia energii elektrycznej. Zaproponowano model matematyczno-komputerowy, który umożliwia uwzględnienie wpływu zmian położenia geograficznego nad poziomem morza oraz warunków meteorologicznych na obszarze, na którym znajdują się gospodarstwa domowe, na zużycie energii. W celu porównania wyników uzyskanych w modelach wyniki porównano z wynikami wartości eksperymentalnych; dla danych eksperymentalnych pobrano odczyty urządzeń pomiarowych energii elektrycznej, które wykazały dużą zbieżność.

Słowa kluczowe

EN

power consumption; mathematical model; computer model; ambient temperature

PL

pobór mocy; model matematyczny; model komputerowy; temperatura otoczenia

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 3
• Strony: 160--163
• Opis fizyczny: Bibliogr. 31 poz., rys., wykr.

Twórcy

autor

Tavarov Saidjon Shiralievich

• Department of Life Safety, South-Ural State University (national research university), Prospekt Lenin, 76, Chelyabinsk, 454080, Russian Federation

• https://orcid.org/0000-0002-2875-2752

autor

Smolin Anton Vyachislavovich

• Department of Life Safety, South-Ural State University (national research university), Prospekt Lenin, 76, Chelyabinsk, 454080, Russian Federation

• https://orcid.org/0000-0001-7511-6807

autor

Safaraliev Murodbek Kholnazarovich

• Department of Automated Electrical Systems, Ural Federal University, 19, Mira Street, Yekaterinburg, 620002, Russian Federation

• https://orcid.org/0000-0003-3433-9742

autor

Kokin Sergey Evgenevich

• Department of Automated Electrical Systems, Ural Federal University, 19, Mira Street, Yekaterinburg, 620002, Russian Federation

• https://orcid.org/0000-0001-7493-172X

autor

Mirakov Ozod Abdulasanovich

• Department of "Electricity supply", academicians Rajabov's avenue 10, 734042, Dushanbe, Republic of Tajikistan

• https://orcid.org/0000-0002-4375-1334

autor

Ahyoev Javod Salamshoevich

• Department of Electric stations, academicians Rajabov's avenue 10, 734042, Dushanbe, Republic of Tajikistan

• https://orcid.org/0000-0002-9869-288X

Bibliografia

• [1] Y. Zakaria, P. Anup. An optimal load schedule of household appliances with leveled load profile and consumer's preferences. International Conference on the Domestic Use of Energy (DUE), Cape Town, South Africa, 2018; pp.1-7.
• [2] Y. Zakaria, Kh. Pule. A binary integer programming model for optimal load scheduling of household appliances with consumer's preferences. International Conference on the Domestic Use of Energy (DUE). Cape Town, South Africa, 2018; pp. 1-8.
• [3] G. Gheorghe, S. Florina. Processing of smart meters data for peak load estimation of consumers. 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE). Bucharest, Romania, 2015; pp. 864 – 867.
• [4] S. Hussein, M. Boonruang. Intelligent Algorithm for Optimal Load Management in Smart Home Appliance Scheduling in Distribution System. International Electrical Engineering Congress (iEECON), Krabi, Thailand, Thailand, 2018; pp. 1-4. http://dx.doi.org/10.1109/IEECON.2018.8712166
• [5] K. Jangkyum. Analysis of power usage at household and proper energy management. International Conference on Information and Communication Technology Convergence (ICTC). Jeju, South Korea, 2018; pp. 450-456.
• [6] I. Fatih, K. Orhan. The Determination of Load Profiles and Power Consumptions of Home Appliances. Energies 2018, 11(3), 607; https://doi.org/10.3390/en11030607.
• [7] A. Leopoldo, B. Francesco, L. Annalisa, S. Rosario, M. Lo, S. Francesco. Smart Power Meters in Augmented Reality Environment for Electricity Consumption Awareness. Energies 2018, 11(9), 2303; https://doi.org/10.3390/en11092303.
• [8] Y. Ke, W. Xudong, D. Yang, J. Ning, H. Haichao, Z. Hangxia. Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy. Energies2018, 11(11), 3089;
• [9] V. Sergej, S. Alina, K. Rima. The Impact of Socio-Economic Indicators on Sustainable Consumption of Domestic Electricity in Lithuania. Sustainability 2018, 10(2), 162; https://doi.org/10.3390/su10020162
• [10] Z. Florian. Load Nowcasting: Predicting Actuals with Limited Data. Energies 2020, 13(6), 1443; https://doi.org/10.3390/en13061443
• [11] N. Aqdas, U. J. Muhammad, J. Nadeem, S. Tanzila, A. Musaed, A. Khursheed. Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids. Energies 2019, 12(5), 866;
• [12] T. Wai-Ming, C. L. Peter Ka, L. Tsz-Ming. Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models-The Case of Hong Kong. Energies 2017, 10(7), 885;
• [13] R. Seunghyoung, N. Jaekoo, K. Hongseok. Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies 2017, 10(1), 3;
• [14] B. I. Makokluev, V. Kostikov. Modeling of electric loads of electric power systems. Electrical Technology Russia 1994, 10, pp. 6-18.
• [15] B. I. Makokluev, V. Pavlikov, A. Vladimirov. Influence of fluctuations of meteorological factors on power consumption of power units. Industrial power engineering 2003, 6, pp. 11-23.
• [16] B. I. Makokluev. Trend of electricity consumption of UES of Russia. Scientific and technical journal. Energy of the unifiednetwork 2019, 5 (48), pp. 6–64.
• [17] B. I. Makokluev, A. S. Polizharov, A. A. Basov, E. Alla Yu., S. V. Loktiono. Short-Term forecasting of power consumption of power systems. Power Technology and Engineering 2018, 4, pp. 24-35.
• [18] B. I. Makokluev, A. S. Polizharov, A.V. Antonov, M. N. Govorun, A.V. Kolesnikov, A. A. Basov, Yu. E. Alla. Operational correction of schedules of electric power consumption in the planning cycle of the balancing market. Power Technology and Engineering 2019, 5, pp. 36-44.
• [19] N. G. Repkina. Research of factors affecting the accuracy prediction daily power consumption. Russian Electromechanics 2015, 2, pp. 41-43. http://dx.doi.org/10.17213/0136-3360-2015-2-41-43
• [20] V. A. Zubakin, N. M. Kovshov. Methods and models for analyzing the volatility of electricity consumption taking intoaccount cyclicality and stochasticity. Analysis, forecast, and management 2015, 7 (15), pp. 6-12.
• [21] S. Komornik, E. Kalichets. Requirements for energy consumption forecasting systems. Energo Market 2008, 3, pp.5-7.
• [22] V. E. Vorotnitsky, Yu. I. Morzhin. Digital transformation of energy in Russia -a system task of the fourthindustrial revolution. Scientific and technical journal. Energy of the unified network 2018, 6(42), pp. 12-21.
• [23] V. E. Vorotnitsky. The Solution to the problems of the Russian electric power industry should be systematic, qualifiedand customer-oriented. Industrial power engineering 2018, 6, pp. 14-21.
• [24] V. E. Vorotnitsky. On digitalization in the economy and electric power industry. Power Technology and Engineering 2019, 12, pp. 6-14.
• [25] G. S. Valeev, M. A. Dzyuba, R. G. Valeev. Modeling of daily load schedules of 6-10 kV distribution network sections in cities and localities under conditions of limited initial information. Bulletin Of SUSU. A Series Of "Energy" 2016, 16 (2), pp. 23-29.
• [26] A.I. Sidorov, S.S. Tavarov. Method for forecasting electric consumption for household users in the conditions of the Republic of Tajikistan. International Journal of Sustainable Development and Planning 2020, Vol. 15, 4, pp. 569-574.
• [27] A. I. Sidorov, O. A. Khanzhina, S. S. Tavarov. Ensuring the Efficiency of Distribution Networks C. Dushanbe and Republic of Tajikistan. International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) 2019. pp. 1-4.
• [28] A. I. Sidorov, S. Sh. Tavarov. Normalization of power consumption in the Republic of Tajikistan taking into account the climatic features of the region. Scientific and technical journal "Energy of the unified network" 2019, 3(45), pp. 70-75.
• [29] S. Sh. Tavarov. Specific power consumption of the domestic sector taking into account the ambient air temperature and the territorial location of the Republic of Tajikistan. Industrial power engineering 2019, 7(7), pp. 19-22.
• [30] SP 256. 1325800.2016. Electrical installations of residential and public buildings rules of design and installation [Electronic resource]. URL: http://files.stroyinf.ru/Data2/1/4293751/4293751598.htm (accessed: 11.07.2017).
• [31] RM-2696-01. Temporary instructions for calculating electrical loads of residential buildings. Moscow. Publishing house GUP "NIAC". 2001. 22 p.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).