Please use this identifier to cite or link to this item: http://ena.lp.edu.ua:8080/handle/ntb/41499
Title: Macromodelling as an aproach to short-term load forecasting of electric power system objects
Other Titles: Макромоделювання як засіб короткотермінового прогнозування енергоспоживання об’єктів електроенергетичних систем
Authors: Гоголюк, Оксана
Козак, Юрій
Наконечний, Тарас
Стахів, Петро
Hoholyuk, Oksana
Kozak, Yuriy
Nakonechnyy, Taras
Stakhiv, Petro
Affiliation: Lviv Polytechnic National University
Bibliographic description (Ukraine): Macromodelling as an aproach to short-term load forecasting of electric power system objects / Oksana Hoholyuk, Yuriy Kozak, Taras Nakonechnyy, Petro Stakhiv // Computational Problems of Electrical Engineering. — Lviv : Lviv Politechnic Publishing House, 2017. — Vol 7. — No 1. — P. 25–32.
Bibliographic description (International): Macromodelling as an aproach to short-term load forecasting of electric power system objects / Oksana Hoholyuk, Yuriy Kozak, Taras Nakonechnyy, Petro Stakhiv // Computational Problems of Electrical Engineering. — Lviv : Lviv Politechnic Publishing House, 2017. — Vol 7. — No 1. — P. 25–32.
Is part of: Computational Problems of Electrical Engineering, 1 (7), 2017
Issue: 1
Volume: 7
Issue Date: 19-Feb-2017
Publisher: Lviv Politechnic Publishing House
Place of the edition/event: Lviv
Keywords: electric power system
macromodel
load forecasting
power consumption
optimization
Number of pages: 8
Page range: 25-32
Start page: 25
End page: 32
Abstract: Розглянуто методи побудови моделей енерго- споживання та описано альтернативний метод прогно- зування енергоспоживання визначених об’єктів з викорис- танням дискретних макромоделей, який дає змогу оціню- вати кількісні характеристики споживання електричної енергії у майбутньому, використовуючи відомі дані натурного експерименту. Описано особливості отримання експериментальних даних та процедуру побудови дискретних автономних макромоделей на їхній основі у вигляді “чорної скриньки” у формі змінних стану. Запропоновано спосіб вибору вектора початкових змінних стану та спосіб його введення у макромодель у зв’язку з відсутністю вектора вхідних змінних у явному вигляді. Обґрунтовано доцільність застосування дискретних автономних макромоделей для короткотермінового енергоспоживання. Наведено отриману макромодель конкретного об’єкта енергоспоживання для короткотермі- нового прогнозування електричного навантаження та виконано верифікацію отриманих результатів.
The paper is concerned with methods for the development of mathematical models intended for electric load forecasting, as well as an alternative method for the forecast of defined objects using discrete macromodels, which allows the quantative characteristics evaluation of future electric energy consumption to be analyzed using known previous data obtained during the field test. There is a description of the features of obtaining the experimental data and the procedure of developing discrete autonomous macromodels based on the data, using the “black box” approach in the form of state variables. A method for choosing the initial variables vector and the way of its introduction into the macromodel is developed because of the absence of an input variables vector in the explicit form. The expedience of applying the discrete autonomous macromodels for short-term electric load forecasting is shown. The developed mactomodel of daily power consumption of the power region served by an electric power substation for short-term electric load forecasting is presented, and the verification of the obtained results is carried out.
URI: http://ena.lp.edu.ua:8080/handle/ntb/41499
ISSN: 2224-0977
Copyright owner: © Національний університет „Львівська політехніка“, 2017
© Hoholyuk О., Kozak Yu., Nakonechnyy T., Stakhiv P., 2017
References (Ukraine): [1] D. Bunn and E. Farmer, Comparative Models for Electric Load Forecasting, New York, USA: Willey, 1985.
[2] H. Alfares and M. Nazeeruddin, Electric load forecasting: literature survey and classification of methods, International Journal of Systems Science, vol. 33, pp. 23–34, 2002.
[3] H. Hippert, C. Pedreira, and R. Souza, “Neural networks for short-term load forecasting: a review and evaluation”, IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44–55, 2001.
[4] Yu. Varestsky, T. Nakonechnyy, M. Fedoniuk, and V. Komar, “Architecture of itelligent monitoring system of power network nonsinusoidal modes”, Naukovi pratsi Vinnytskogo natsionalnoho tekhnichnoho universytetu, no. 1, pp. 1–10, Vinnytsia, Ukraine, 2010. (Ukrainian)
[5] P. Chernenko and O. Martynyuk, “Enhancing the Effectiveness of Short-Term Forecasting of Electric Load of United Power System”, Tekhnichna elektrodynamika, no. 1, pp. 63–70, Kyiv, Ukraine: Institute of Electrodynamics of Ukraine, 2012.
[6] A. Singh, S. Ibraheem, and M. Muazzam, “An Overview of Electricity Demand Forecasting Techniques”, Network and Complex Systems, vol. 3, no. 3, pp. 38–48, 2013.
[7] S. Soliman and A. Al-Kandan, Electrical load forecasting: modeling and model construction, Oxford, UK: Butterworth-Heinemann, 2010.
[8] P. Stakhiv, Y. Kozak, and O. Hoholyuk, “Effectiveness Evaluation of Discrete Macromodelling to Forecast Power Consumption of Electric Power Systems Component Elements, Computational problems of electrical engineering, vol. 6, no. 1, pp. 45–48, 2016.
[9] Yu. Kozak, “Modification of the Rastrigin’s director cone method”. In Elektronika i sviaz: Problemy fizicheskoy i biomeditsinskoy elektroniki, p. 424, 1997. (Ukrainian)
References (International): [1] D. Bunn and E. Farmer, Comparative Models for Electric Load Forecasting, New York, USA: Willey, 1985.
[2] H. Alfares and M. Nazeeruddin, Electric load forecasting: literature survey and classification of methods, International Journal of Systems Science, vol. 33, pp. 23–34, 2002.
[3] H. Hippert, C. Pedreira, and R. Souza, "Neural networks for short-term load forecasting: a review and evaluation", IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44–55, 2001.
[4] Yu. Varestsky, T. Nakonechnyy, M. Fedoniuk, and V. Komar, "Architecture of itelligent monitoring system of power network nonsinusoidal modes", Naukovi pratsi Vinnytskogo natsionalnoho tekhnichnoho universytetu, no. 1, pp. 1–10, Vinnytsia, Ukraine, 2010. (Ukrainian)
[5] P. Chernenko and O. Martynyuk, "Enhancing the Effectiveness of Short-Term Forecasting of Electric Load of United Power System", Tekhnichna elektrodynamika, no. 1, pp. 63–70, Kyiv, Ukraine: Institute of Electrodynamics of Ukraine, 2012.
[6] A. Singh, S. Ibraheem, and M. Muazzam, "An Overview of Electricity Demand Forecasting Techniques", Network and Complex Systems, vol. 3, no. 3, pp. 38–48, 2013.
[7] S. Soliman and A. Al-Kandan, Electrical load forecasting: modeling and model construction, Oxford, UK: Butterworth-Heinemann, 2010.
[8] P. Stakhiv, Y. Kozak, and O. Hoholyuk, "Effectiveness Evaluation of Discrete Macromodelling to Forecast Power Consumption of Electric Power Systems Component Elements, Computational problems of electrical engineering, vol. 6, no. 1, pp. 45–48, 2016.
[9] Yu. Kozak, "Modification of the Rastrigin’s director cone method". In Elektronika i sviaz: Problemy fizicheskoy i biomeditsinskoy elektroniki, p. 424, 1997. (Ukrainian)
Content type: Article
Appears in Collections:Computational Problems Of Electrical Engineering. – 2017 – Vol. 7, No. 1



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