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The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

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Abstract

The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network’s functioning are proposed.

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References

  1. E. Merkel, R. McKenna, and W. Fichtner, “Optimization of the capacity and the dispatch of decentralized micro-CHP systems: A case study for the UK,” Appl. Energy 140, 120–134 (2015).

    Article  Google Scholar 

  2. A. Maleki and F. Pourfayaz, “Sizing of stand-alone photovoltaic/wind/diesel system with battery and fuel cell storage devices by harmony search algorithm,” J. Energy Storage 2, 30–42 (2015).

    Article  Google Scholar 

  3. A. D. Smith, P. J. Mago, and N. Fumo, “Benefits of thermal energy storage option combined with CHP system for different commercial building types,” Sustainable Energy Technol. Assess. 1 (1), 3–12 (2013).

    Article  Google Scholar 

  4. O. A. Ivanin and L. B. Director, “The solution of the optimization problem of small energy complexes using linear programming methods,” J. Phys.: Conf. Ser. 774, 012046 (2016). doi 10.1088/1742-6596/774/1/012046

    Google Scholar 

  5. H. Li, S. Guo, H. Zhao, C. Su, and B. Wang, “Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm,” Energies 5, 4430–4445 (2012).

    Article  Google Scholar 

  6. M. L. Huang, “Hybridization of chaotic quantum particle swarm optimization with SVR in electric demand forecasting,” Energies 9 (6), 426–441 (2016).

    Article  Google Scholar 

  7. I. M. Arkybaev, “Prediction of electrical loads using neural networks,” Nauka. Obraz. Tekh., No. 2, 153–160 (2015).

    Google Scholar 

  8. A. G. Saidkhodzhaev, “The new method for determining the electrical loads on the basis of interval analogues interpolation splines,” Avtom. Tekhnol. Proizvod., No. 2, 28–30 (2015).

    Google Scholar 

  9. L. A. Myasoedova, Yu. V. Myasoedov, and N. V. Savina, “Modeling the current load in active-adaptive distribution electric grids,” in Proc. Power Generation: Control, Quality, and Efficiency of Resource Use, Blagoveshchensk Sci. Conf., May 27–29, 2015, pp. 33–40.

  10. A. V. Voloshko, T. M. Lutchin, and A. M. Klad’ko, “Short-term forecasting of electric load schedules on basis of wavelet-transform,” Energosberezhenie. Energetika. Energoaudit, No. 6(100), 35–42 (2012).

    Google Scholar 

  11. P. E. Eshtokina, D. S. Pasechnaya, and I. I. Nadtoka, “Analysis of characteristics of random electric load charts of multi-apartment buildings with electric stoves,” in Proc. Modern Power Generation Systems and Their Control Complexes Sci. Conf., Novocherkassk, June 25, 2015, pp. 28–34.

  12. D. Yu. Taranov, A. V. Pavlov, Nadtoka I. I. “Analysis of the random component of the daily electric load charts of multi-apartment buildings,” in Proc. Modern Power Generation Systems and Their Control Complexes Sci. Conf., Novocherkassk, June 25, 2015, pp. 42–48.

  13. N. S. Morozova, Methods and Models of Forecasting Power Consumption and Electric Loads of Systems (Omsk. Gos. Tekh. Univ., Omsk, 2015).

    Google Scholar 

  14. E. Ya. Sokolov, Heat Generation and Heating Networks (Mosk. Energ. Inst., Moscow, 1999).

    Google Scholar 

  15. On Affirmation of the Rules for Establishing and Determining Standards for the Consumption of Communal Services and Standards for the Consumption of Communal Resources for the Purpose of Maintaining Common Property in an Apartment Building, RF Government Decree No. 306 of May 23, 2006.

  16. A. S. Bondarchuk and D. P. Nizova, “Comparison of the theoretical and the actual rated unit electric loadingof the in the city of Odessa,” Tr. Odes. Politekh. Univ., No. 1, 78–81 (2011).

    Google Scholar 

  17. A. Yu. Kotel’nikova and A. S. Vanin, “Methods of short-term load forecasting for application in intellectual electric grids,” in Proc. Energy Performance and Energy Safety of Production Processes Sci. Conf., Tol’yatti, Apr. 12–14, 2016, pp. 122–125.

    Google Scholar 

  18. S. Khaikin, Neural Networks: A Complete Course, 2-nd ed. (ID Vil’yams, Moscow, 2006).

    Google Scholar 

  19. Tao Hong, “Winning methods from npower forecasting challenge 2016,” http://blog.drhongtao.com/2016/12/winning-methods-fromnpower-forecasting-challenge-2016.html. Accessed November 28, 2017.

    Google Scholar 

  20. Rp5.ru. Weather Forecast. https://rp5.ru/Pogoda_ v_Sangare,_Respublika_Sakha_(Yakutiya). Accessed August 25, 2017.

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Authors and Affiliations

  1. Joint Institute for High Temperatures, Russian Academy of Sciences, Moscow, 125412, Russia

    O. A. Ivanin & L. B. Direktor

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  1. O. A. Ivanin
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  2. L. B. Direktor
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Correspondence to O. A. Ivanin.

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Original Russian Text © O.A. Ivanin, L.B. Direktor, 2018, published in Teploenergetika.

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Ivanin, O.A., Direktor, L.B. The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers. Therm. Eng. 65, 258–265 (2018). https://doi.org/10.1134/S004060151805004X

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  • DOI: https://doi.org/10.1134/S004060151805004X

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