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Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic

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  • Khasanzoda, Nasrullo
  • Zicmane, Inga
  • Beryozkina, Svetlana
  • Safaraliev, Murodbek
  • Sultonov, Sherkhon
  • Kirgizov, Alifbek

Abstract

Renewable energy integration becomes an important criterion for the sustainable generation of electrical power. The high introduction of wind power plants into the power system leads to some inconveniences in the power system operators’ work due to the unpredictable and variable nature of the wind speed and the power generated by wind farms. Even though the power generated at the wind power plants is not regulated by the system operator, the accurate predicting of the wind speed and the angle of its direction could solve such problems leading to improving the reliability of power supply systems.

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  • Khasanzoda, Nasrullo & Zicmane, Inga & Beryozkina, Svetlana & Safaraliev, Murodbek & Sultonov, Sherkhon & Kirgizov, Alifbek, 2022. "Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic," Renewable Energy, Elsevier, vol. 191(C), pages 723-731.
    • Handle: RePEc:eee:renene:v:191:y:2022:i:c:p:723-731
    DOI: 10.1016/j.renene.2022.04.017
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    References listed on IDEAS

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    Cited by:

    1. Khasanzoda, Nasrullo & Safaraliev, Murodbek & Zicmane, Inga & Beryozkina, Svetlana & Rahimov, Jamshed & Ahyoev, Javod, 2022. "Use of smart grid based wind resources in isolated power systems," Energy, Elsevier, vol. 253(C).
    2. Liang, Hejun & Pirouzi, Sasan, 2024. "Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources," Energy, Elsevier, vol. 293(C).
    3. 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.
    4. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.

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