{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T06:14:52Z","timestamp":1778998492553,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Russian Federation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sustainability"],"abstract":"<jats:p>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\u201328% 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\u2013September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply.<\/jats:p>","DOI":"10.3390\/su15021730","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T01:41:20Z","timestamp":1673919680000},"page":"1730","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems"],"prefix":"10.3390","volume":"15","author":[{"given":"Vadim","family":"Manusov","sequence":"first","affiliation":[{"name":"Department of Power Supply Systems, Novosibirsk State Technical University, 20 K. Marx Ave., 630073 Novosibirsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5704-0976","authenticated-orcid":false,"given":"Pavel","family":"Matrenin","sequence":"additional","affiliation":[{"name":"Ural Power Engineering Institute, Ural Federal University, 19 Mira Str., 620002 Yekaterinburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muso","family":"Nazarov","sequence":"additional","affiliation":[{"name":"Department of Power Supply Systems, Novosibirsk State Technical University, 20 K. Marx Ave., 630073 Novosibirsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1593-129X","authenticated-orcid":false,"given":"Svetlana","family":"Beryozkina","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3433-9742","authenticated-orcid":false,"given":"Murodbek","family":"Safaraliev","sequence":"additional","affiliation":[{"name":"Department of Automated Electrical Systems, Ural Federal University, 19 Mira Str., 620002 Yekaterinburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3378-0731","authenticated-orcid":false,"given":"Inga","family":"Zicmane","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Environmental Engineering, Riga Technical University, 12\/1 Azenes Str., 1048 Riga, Latvia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4344-6462","authenticated-orcid":false,"given":"Anvari","family":"Ghulomzoda","sequence":"additional","affiliation":[{"name":"Department of Automated Electric Power Systems, Novosibirsk State Technical University, 630073 Novosibirsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Burton, T., Jenkins, N., Bossanyi, E., Sharpe, D., and Graham, M. (2021). The nature of the wind. Wind Energy Handbook, John Wiley & Sons. [3rd ed.].","DOI":"10.1002\/9781119451143"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moreno-Munoz, A. (2017). Description of wind power forecasting systems. Large Scale Grid Integration of Renewable Energy Sources, Institution of Engineering and Technology (The IET).","DOI":"10.1049\/PBPO098E"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.egypro.2017.03.517","article-title":"Optimal Hybrid Power System Using Renewables and Hydrogen for an Isolated Island in the UK","volume":"105","author":"Kennedy","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.rser.2018.04.105","article-title":"A review on the utilization of hybrid renewable energy","volume":"91","author":"Guo","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"47332","DOI":"10.1109\/ACCESS.2018.2867276","article-title":"Multi-Objective Optimization of Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis for Islanding and Grid Connected Modes of Operation","volume":"6","author":"Singh","year":"2018","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.solener.2007.12.002","article-title":"A new hybrid ocean thermal energy conversion-offshore solar pond (OTEC-OSP) design: A cost optimization approach","volume":"82","author":"Straatman","year":"2008","journal-title":"Sol. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.renene.2006.01.002","article-title":"Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure","volume":"32","author":"Mellit","year":"2007","journal-title":"Renew. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1016\/j.enconman.2007.07.015","article-title":"Pumping station design for a pumped-storage wind\u2013hydro power plant","volume":"48","author":"Anagnostopoulos","year":"2007","journal-title":"Energy Convers. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1016\/j.rser.2011.11.011","article-title":"Optimum design of hybrid renewable energy systems: Overview of different approaches","volume":"16","author":"Erdinc","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1016\/j.apenergy.2019.05.016","article-title":"Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems","volume":"250","author":"Zhou","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2011.05.033","article-title":"Current methods and advances in forecasting of wind power generation","volume":"37","author":"Foley","year":"2012","journal-title":"Renew. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.apenergy.2012.03.054","article-title":"Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output","volume":"99","author":"Cassola","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_13","first-page":"1589","article-title":"A High-Accuracy Wind Power Forecasting Model","volume":"32","author":"Fang","year":"2017","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.apenergy.2010.10.031","article-title":"ARMA based approaches for forecasting the tuple of wind speed and direction","volume":"88","author":"Erdem","year":"2011","journal-title":"Appl. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"022015","DOI":"10.1088\/1755-1315\/199\/2\/022015","article-title":"Wind power forecasting based on time series ARMA model","volume":"199","author":"Wang","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.3390\/en7063710","article-title":"Exponential smoothing approaches for prediction in real-time electricity markets","volume":"7","author":"Jonsson","year":"2014","journal-title":"Energies"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cadenas, E., Rivera, W., Campos-Amezcua, R., and Heard, C. (2016). Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. Energies, 9.","DOI":"10.3390\/en9020109"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lei, C., and Ran, L. (2008, January 6\u20139). Short-term wind speed forecasting model for wind farm based on wavelet decomposition. Proceedings of the 3rd International Conference Electronic Utility Deregulation Restructuring Power Technology, Nanjing, China.","DOI":"10.1109\/DRPT.2008.4523836"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rser.2014.03.033","article-title":"A review of combined approaches for prediction of short-term wind speed and power","volume":"34","author":"Tascikaraoglu","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.renene.2019.04.157","article-title":"A novel hybrid system based on multi-objective optimization for wind speed forecasting","volume":"146","author":"Wu","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.enconman.2018.07.070","article-title":"A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm","volume":"173","author":"Hu","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_22","first-page":"146","article-title":"Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system","volume":"25","author":"Khosravi","year":"2018","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yadav, A.K., and Malik, H. (2019). Short-term wind speed forecasting for power generation in Hamirpur, Himachal Pradesh, India, using artificial neural networks. Applications of Artificial Intelligence Techniques in Engineering, Springer.","DOI":"10.1007\/978-981-13-1822-1_24"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.enconman.2014.10.001","article-title":"Wind power forecast using wavelet neural network trained by improved clonal selection algorithm","volume":"89","author":"Chitsaz","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TNNLS.2013.2276053","article-title":"Short-term load and wind power forecasting using neural network-based prediction intervals","volume":"25","author":"Quan","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.renene.2014.09.058","article-title":"Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information","volume":"75","author":"Osorio","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.apenergy.2018.11.012","article-title":"A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting","volume":"235","author":"Jiang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, T., Jin, Y., and Gao, Y. (2019). A new hybrid approach for short-term electric load forecasting applying support vector machine with ensemble empirical mode decomposition and whale optimization. Energies, 12.","DOI":"10.3390\/en12081520"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.enconman.2016.01.007","article-title":"Linear and non-linear autoregressive models for short-term wind speed forecasting","volume":"112","author":"Lydia","year":"2016","journal-title":"Energy Convers. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"118777","DOI":"10.1016\/j.apenergy.2022.118777","article-title":"A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting","volume":"312","author":"Han","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112345","DOI":"10.1016\/j.enconman.2019.112345","article-title":"Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction","volume":"205","author":"Ma","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1007\/s42452-020-2830-0","article-title":"Estimating the short-term and long-term wind speeds: Implementing hybrid models through coupling machine learning and linear time series models","volume":"2","author":"Mehdizadeh","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/TPWRS.2006.873421","article-title":"Very short-term wind forecasting for Tasmanian power generation","volume":"21","author":"Potter","year":"2006","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/ICJECE.2022.3152524","article-title":"An Effective Very Short-Term Wind Speed Prediction Approach Using Multiple Regression Models","volume":"45","author":"Mogos","year":"2022","journal-title":"IEEE Can. J. Electr. Comput. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.renene.2018.02.092","article-title":"An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm","volume":"123","author":"Liu","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.energy.2016.10.040","article-title":"Short-term wind speed forecasting using a hybrid model","volume":"119","author":"Jiang","year":"2017","journal-title":"Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.energy.2017.04.094","article-title":"Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine","volume":"129","author":"Yuan","year":"2017","journal-title":"Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/ACCESS.2021.3137419","article-title":"Critical Review of Data, Models and Performance Metrics for Wind and Solar Power Forecast","volume":"10","author":"Prema","year":"2022","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.renene.2019.01.031","article-title":"Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting","volume":"136","author":"Mohapatra","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.renene.2013.08.011","article-title":"Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm","volume":"62","author":"Liu","year":"2014","journal-title":"Renew. Energy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.renene.2012.07.022","article-title":"A hybrid strategy of short term wind power prediction","volume":"50","author":"Peng","year":"2013","journal-title":"Renew. Energy"},{"key":"ref_42","first-page":"21","article-title":"Implementation of a topographic artificial neural network wind speed prediction model for assessing onshore wind power potential in Sibu, Sarawak","volume":"23","author":"Lawan","year":"2020","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.renene.2022.04.017","article-title":"Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic","volume":"191","author":"Khasanzoda","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.egyr.2022.09.164","article-title":"Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change","volume":"8","author":"Safaraliev","year":"2022","journal-title":"Energy Rep."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Manusov, V., Beryozkina, S., Nazarov, M., Safaraliev, M., Zicmane, I., Matrenin, P., and Ghulomzoda, A. (2022). Optimal Management of Energy Consumption in an Autonomous Power System Considering Alternative Energy Sources. Mathematics, 10.","DOI":"10.3390\/math10030525"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ghulomzoda, A., Safaraliev, M., Matrenin, P., Beryozkina, S., Zicmane, I., Gubin, P., Gulyamov, K., and Saidov, N. (2021). A Novel Approach of Synchronization of Microgrid with a Power System of Limited Capacity. Sustainability, 13.","DOI":"10.3390\/su132413975"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.egyr.2021.11.112","article-title":"Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change","volume":"8","author":"Matrenin","year":"2022","journal-title":"Energy Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"270","DOI":"10.3103\/S0003701X20040118","article-title":"Energy Potential Estimation of the Region\u2019s Solar Radiation Using a Solar Tracker","volume":"56","author":"Safaraliev","year":"2020","journal-title":"Appl. Sol. Energy"},{"key":"ref_49","first-page":"3682","article-title":"Expert system application for reactive power compensation in isolated electric power systems","volume":"5","author":"Kirgizov","year":"2021","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1016\/j.egyr.2021.05.014","article-title":"Renewable energy in Central Asia: An overview of potentials, deployment, outlook, and barriers","volume":"7","author":"Laldjebaev","year":"2021","journal-title":"Energy Rep."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sangov, K.S., and Chorshanbiev, S.N. (2022, January 25\u201328). Integration of Renewable Energy Sources into the Power Supply System of the Murghab Settlement, Gorno-Badakhshan Autonomous Region. Proceedings of the Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), Saint Petersburg, Russia.","DOI":"10.1109\/ElConRus54750.2022.9755454"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5757","DOI":"10.1016\/j.ijhydene.2021.12.002","article-title":"Electromagnetic transients in the control system of output parameters of a solar power plant in Tajikistan Central Asia region","volume":"47","author":"Sharifov","year":"2022","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Matrenin, P.V., Manusov, V.Z., Khalyasmaa, A.I., Antonenkov, D.V., Eroshenko, S.A., and Butusov, D.N. (2020). Improving Accuracy and Generalization Performance of Small-Size Recurrent Neural Networks Applied to Short-Term Load Forecasting. Mathematics, 8.","DOI":"10.3390\/math8122169"}],"container-title":["Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2071-1050\/15\/2\/1730\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:07:42Z","timestamp":1760119662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2071-1050\/15\/2\/1730"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["su15021730"],"URL":"https:\/\/doi.org\/10.3390\/su15021730","relation":{},"ISSN":["2071-1050"],"issn-type":[{"value":"2071-1050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,16]]}}}