uzbekhydroenergetics “O‘ZBEKGIDROENERGETIKA” ilmiy-texnik jurnal O‘zbekgidroenergetika 3060-4672 O‘zbekgidroenergetika 10.5281/zenodo.15044746 REL-A-014080 GIDROELEKTR STANSIYALARDA QUVVAT PROGNOZLASHDA SUN‘IY INTELLEKTDAN FOYDALANISH USING ARTIFICIAL INTELLIGENCE FOR CAPACITY FORECASTING IN HYDROELECTRIC POWER PLANTS ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ПРОГНОЗИРОВАНИЯ МОЩНОСТИ В ГИДРОЭЛЕКТРОСТАНЦИЯХ Soxibjon KAMOLIDDINOV 01 01 2024 2024 6 4 56 63 https://creativecommons.org/licenses/by/4.0

Ushbu maqolada GESlarda ishlab chiqarilayotgan quvvatniprognozlashda ishlatiladigan sun‘iy intelekt va neyron tarmoqlar yordamida bajarilganmaqolalar o‘rganilgan. O‘rganish natijasida har bir maqolada ko‘rilgan metodlar vayondashuvlar, ularni afzalliklari, hamda kamchiliklari yoritilgan va maqola oxiridaxulosalar berilgan.

This article examines articles writtenusing artifcial intelligence and neural networksused to forecast hydroelectric power generation.Based on the results of the study, each articlehighlights methods and approaches, theiradvantages and disadvantages, and providesconclusions at the end of the article.

В данной статье изучаются статьи,выполненные с использованием искусственногоинтеллекта и нейронных сетей, используемых припрогнозировании выработки электроэнергии нагидроэлектростанциях. По итогам исследованияв каждой статье выделены методы и подходы, ихпреимущества и недостатки, а в конце статьиприведены выводы

 GES sun‘iy intellekt ANN ANFIS DELM prognozlash model HPP artifcial intelligence ANN ANFIS DELM forecasting model ГЭС искусственный интеллект ANN ANFIS DELM прогнозирование модель Senthil Kumar A.R., Goyal M.K., Ojha C.S.P., Singh R.D., Swamee P.K. Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India. Water Sci Technology 2013; 68:2521– https://doi.org/10.2166/wst.2013.491. PRINT ISSN 3060-4672 O‘ZBEKGIDROENERGETIKA VOLUME 6 | ISSUE 4 | 2024 УЗБЕKГИДРОЭНЕРГЕТИКА UZBEKHYDROPOWER NAZARIYA VA AMALIYOT Fayaz M, Kim D. A prediction methodology of energy consumption based on deep extreme learning machine and comparative analysis in residential buildings. Electron 2018; 7. https://doi.org/10.3390/electronics7100222. Shamshirband S., Mosavi A., Chau K. Sensitivity study of ANFIS model parameters to predict the pressure gradient with combined input and outputs hydrodynamics parameters in the bubble column reactor 2019. https://doi. org/10.20944/preprints201905.0044.v1. Lounis H., Boukadoum M., Siveton V. Assessing Hydro Power System Relevant Variables: a Comparison Between a Neural Network and Different Machine Learning approaches 2002: 45–51. Egoigwe S.V., Chukwudozie C., Nwobi C., Araoye T.O., Arize C.I., Anoliefo E.C. Application of a Fuzzy Logic Controller for Hydropower Generator Speed Regulation. Eur J Eng Res Sci 2019; 4:132–5. https://doi.org/10.24018/ ejers.2019.4.3.1203. Luna I., Hidalgo I.G., Pedro P.S.M., Barbosa P.S.F., Francato A.L., Correia P.B. Fuzzy inference systems for multi-step ahead daily inflow forecasting. PesquiOperacional 2017; 37:129–44. https://doi.org/10.1590/0101- 7438.2017.037.01.0129. 10.1590/0101-7438.2017.037.01.0129 Journal E., Studies E., Vol M. Modelling of Hydropower Reservoir Variables for Energy Generation: Neural Network Approach. Abdulkadir, T.S., 1 SALAMI, A.W., 1 Anwar, A.R. 1 and Kareem, A. G. 2. Ethiop J. Environ Stud Manag Vol 2013; 6:310–6. Li G., Sun Y., He Y., Li X., Tu Q. Short-term power generation energy forecasting model for small hydropower stations using GA-SVM. Math ProblEng 2014; 2014. https://doi.org/10.1155/2014/381387 Stokelj T., Golob R., Application of Neural Networks For Hydro Power Plant Water Inflow Forecasting, Infl W. Zs, 1000 2000:189–93. Shaktawat A., Vadhera S. Fuzzy logic based determination of cost overrun of hydro power plant. Int Conf Electr Power Energy Syst ICEPES 2016 2017:301–4. https://doi.org/10.1109/ICEPES.2016.7915947. Li L., Yao F., Huang Y., Zhou F. Hydropower generation forecasting via deep neural network. Proc - 2019 6th Int Conf Inf Sci Control Eng ICISCE 2019 2019:324– https://doi.org/10.1109/ICISCE48695.2019.00071. Mamlook R., Akash B.A., Mohsen M.S. A neuro-fuzzy program approach for evaluating electric power generation systems. Energy 2001; 26:619–32. https:// doi.org/10.1016/S0360-5442(01)00015-9. Valizadeh N., El-Shafie A., Mukhlisin M., El-Shafie A.H. Daily water level forecasting using adaptive neuro-fuzzy interface system with different scenarios: Klang Gate, Malaysia. Int J Phys Sci 2011; 6:7379–89. https://doi.org/10.5897/ IJPS11.1314. Oltean G., Gordan M., EŞIPO Ş., Ncel ESTĂ, Dancea O. Electronics and Telecommunications Fuzzy Logic Based Decision Making for Hydroelectric Energy Generation in A Cascaded Hydropowerplants Electronics and Telecommunications 2009; 50:42–8. O‘ZBEKGIDROENERGETIKA PRINT ISSN 3060-4672 62 УЗБЕKГИДРОЭНЕРГЕТИКА UZBEKHYDROPOWER VOLUME 6 | ISSUE 4 | 2024 ТЕОРИЯ И ПРАКТИКА THEORY AND PRACTICE Cheng C.T., Lin J.Y., Sun Y.G., Chau K. Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models. Lect Notes Comput Sci 2005; 3612:1152–61. https://doi. org/10.1007/11539902_145. Dehghani M., Riahi-Madvar H., Hooshyaripor F., Mosavi A., Shamshirband S., Zavadskas E.K., et al. prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system. Energies 2019; 12:1–20. https://doi.org/10.3390/en12020289. Kucukali S. Risk assessment of river-type hydropower plants using fuzzy logic approach. Energy Policy 2011;39:6683–8.https://doi.org/10.1016/j. enpol.2011.06.067. Ghosh S., Majumder M., Pal M. Development of performance index for evaluation of small-scale hydro power plants by neural network and multi- criteria decision making. Int J Energy Stat 2016; 04:1650019. https://doi.org/10.1142/ s2335680416500198. Alrayess H., Gharbia S., Beden N., Keskin A.U. Using Machine Learning Techniques and Deep Learning in Forecasting The Hydroelectric Power Generation in Almus Dam, Turkey. 5th Int Symp Dam Saf 2018:635–47. Hammid A.T., Sulaiman M.H. Bin, Abdalla A.N. Prediction of small hydropower plant power production in Himreen Lake dam (HLD) using artificial neural network. Alexandria Eng J 2018; 57:211–21. https://doi.org/10.1016/j. aej.2016.12.011. Stokelj T., Paravan D., Golob R. Enhanced artificial neural network inflow forecasting algorithm for run-of-river hydropower plants. J Water Resour Plan Manag 2002; 128:415–23. Feng G., Xia G., Chen B., Xiao Y., Zhou R. A method for rockburst prediction in the deep tunnels of hydropower stations based on the monitored microseismicity and an optimized probabilistic neural network model. Sustain 2019; 11. https://doi.org/10.3390/su11113212. Jalalkamali A., Sedghi H., Manshouri M. Monthly groundwater level prediction using ANN and neuro-fuzzy models: A case study on Kerman plain, Iran. J Hydroinformatics 2011; 13:867–76. https://doi.org/10.2166/hydro.2010.034. Kumar K., Saini R.P. Materials Today: Proceedings Application of machine learning for hydropower plant silt data analysis. Mater Today Proc 2020. https:// doi.org/10.1016/j.matpr.2020.09.375. PRINT ISSN 3060-4672 O‘ZBEKGIDROENERGETIKA VOLUME 6 | ISSUE 4 | 2024 УЗБЕKГИДРОЭНЕРГЕТИКА UZBEKHYDROPOWER