Macromodelling as an aproach to short-term load forecasting of electric power system objects

Simple item page
dc.citation.epage32
dc.citation.issue1
dc.citation.spage25
dc.citation.volume7
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorГоголюк, Оксана
dc.contributor.authorКозак, Юрій
dc.contributor.authorНаконечний, Тарас
dc.contributor.authorСтахів, Петро
dc.contributor.authorHoholyuk, Oksana
dc.contributor.authorKozak, Yuriy
dc.contributor.authorNakonechnyy, Taras
dc.contributor.authorStakhiv, Petro
dc.coverage.placenameLviv
dc.date.accessioned2018-06-07T11:41:55Z
dc.date.available2018-06-07T11:41:55Z
dc.date.created2017-02-19
dc.date.issued2017-02-19
dc.description.abstractРозглянуто 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.
dc.description.abstractThe 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.
dc.format.extent25-32
dc.format.pages8
dc.identifier.citationMacromodelling 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.
dc.identifier.citationenMacromodelling 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.
dc.identifier.issn2224-0977
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/41499
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Problems of Electrical Engineering, 1 (7), 2017
dc.relation.references[1] D. Bunn and E. Farmer, Comparative Models for Electric Load Forecasting, New York, USA: Willey, 1985.
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[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)
dc.relation.references[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.
dc.relation.references[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.
dc.relation.references[7] S. Soliman and A. Al-Kandan, Electrical load forecasting: modeling and model construction, Oxford, UK: Butterworth-Heinemann, 2010.
dc.relation.references[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.
dc.relation.references[9] Yu. Kozak, “Modification of the Rastrigin’s director cone method”. In Elektronika i sviaz: Problemy fizicheskoy i biomeditsinskoy elektroniki, p. 424, 1997. (Ukrainian)
dc.relation.referencesen[1] D. Bunn and E. Farmer, Comparative Models for Electric Load Forecasting, New York, USA: Willey, 1985.
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[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)
dc.relation.referencesen[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.
dc.relation.referencesen[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.
dc.relation.referencesen[7] S. Soliman and A. Al-Kandan, Electrical load forecasting: modeling and model construction, Oxford, UK: Butterworth-Heinemann, 2010.
dc.relation.referencesen[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.
dc.relation.referencesen[9] Yu. Kozak, "Modification of the Rastrigin’s director cone method". In Elektronika i sviaz: Problemy fizicheskoy i biomeditsinskoy elektroniki, p. 424, 1997. (Ukrainian)
dc.rights.holder© Національний університет „Львівська політехніка“, 2017
dc.rights.holder© Hoholyuk О., Kozak Yu., Nakonechnyy T., Stakhiv P., 2017
dc.subjectelectric power system
dc.subjectmacromodel
dc.subjectload forecasting
dc.subjectpower consumption
dc.subjectoptimization
dc.titleMacromodelling as an aproach to short-term load forecasting of electric power system objects
dc.title.alternativeМакромоделювання як засіб короткотермінового прогнозування енергоспоживання об’єктів електроенергетичних систем
dc.typeArticle

Files

Original bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
2017v7n1_Hoholyuk_O-Macromodelling_as_an_aproach_25-32.pdf
Size:
399.23 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
2017v7n1_Hoholyuk_O-Macromodelling_as_an_aproach_25-32__COVER.png
Size:
527.43 KB
Format:
Portable Network Graphics
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.09 KB
Format:
Plain Text
Description:
Download

Collections

Computational Problems Of Electrical Engineering. – 2017 – Vol. 7, No. 1