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Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems

Author

Listed:
  • Vadim Manusov

    (Department of Power Supply Systems, Novosibirsk State Technical University, 20 K. Marx Ave., 630073 Novosibirsk, Russia)

  • Pavel Matrenin

    (Ural Power Engineering Institute, Ural Federal University, 19 Mira Str., 620002 Yekaterinburg, Russia)

  • Muso Nazarov

    (Department of Power Supply Systems, Novosibirsk State Technical University, 20 K. Marx Ave., 630073 Novosibirsk, Russia)

  • Svetlana Beryozkina

    (College of Engineering and Technology, American University of the Middle East, Kuwait)

  • Murodbek Safaraliev

    (Department of Automated Electrical Systems, Ural Federal University, 19 Mira Str., 620002 Yekaterinburg, Russia)

  • Inga Zicmane

    (Faculty of Electrical and Environmental Engineering, Riga Technical University, 12/1 Azenes Str., 1048 Riga, Latvia)

  • Anvari Ghulomzoda

    (Department of Automated Electric Power Systems, Novosibirsk State Technical University, 630073 Novosibirsk, Russia)

Abstract

Predicting the variability of wind energy resources at different time scales is extremely important for effective energy management. The need to obtain the most accurate forecast of wind speed due to its high degree of volatility is particularly acute since this can significantly improve the planning of wind energy production, reduce costs and improve the use of resources. In this study, a method for predicting the speed of wind flow in an isolated power system of the Gorno-Badakhshan Autonomous Oblast (GBAO), based on the use of a neural network with a learning process control algorithm, is proposed. Predicting is performed for four seasons of the year, based on hourly retrospective meteorological data of wind speed observations. The obtained wind speed average error forecasting ranged from 20–28% for a day ahead. The prediction results serve as a basis for optimizing the energy consumption of individual generating consumers to minimize their financial and technical costs. In addition, this study takes into account the possibility of exporting electricity to a neighboring country as an additional income line for the isolated GBAO power system during periods of excess energy from hydropower plants (March–September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply.

Suggested Citation

  • Vadim Manusov & Pavel Matrenin & Muso Nazarov & Svetlana Beryozkina & Murodbek Safaraliev & Inga Zicmane & Anvari Ghulomzoda, 2023. "Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1730-:d:1037891
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    References listed on IDEAS

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