{"title":"Models and methods for forecasting electrical loads","authors":"G. Tolegenova, A. Zakirova, A. Astankevich","doi":"10.32523/2616-7263-2023-143-2-260-268","DOIUrl":null,"url":null,"abstract":"Currently, the prediction of electrical loads is an important task. On the basis of forecasts, the operating modes of stations, the network configuration are calculated, the efficiency and quality of electric power is estimated, the schedule of repair work is calculated, etc. The electric load forecasting model is one of the foresight tools for making management decisions when managing electric power systems. This article consists in the construction, evaluation and comparative study of various models for forecasting electricity consumption. The following approaches and methods in forecasting were studied and analyzed: neural network, neuro fuzzy.","PeriodicalId":168248,"journal":{"name":"BULLETIN of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BULLETIN of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32523/2616-7263-2023-143-2-260-268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Currently, the prediction of electrical loads is an important task. On the basis of forecasts, the operating modes of stations, the network configuration are calculated, the efficiency and quality of electric power is estimated, the schedule of repair work is calculated, etc. The electric load forecasting model is one of the foresight tools for making management decisions when managing electric power systems. This article consists in the construction, evaluation and comparative study of various models for forecasting electricity consumption. The following approaches and methods in forecasting were studied and analyzed: neural network, neuro fuzzy.