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

Vadim Manusov, Pavel Matrenin, Muso Nazarov, Svetlana Beryozkina (), Murodbek Safaraliev, Inga Zicmane and Anvari Ghulomzoda
Additional contact information
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

Sustainability, 2023, vol. 15, issue 2, 1-12

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.

Keywords: isolated power system; neural networks; prediction; wind speed (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/2/1730/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/2/1730/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:2:p:1730-:d:1037891

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().