{"title":"欧洲能源市场的智能风险管理系统","authors":"O.A. Poplavskyi, O.I. Soroka, M.O. Litvin, A.V. Poplavskyi","doi":"10.31649/1681-7893-2024-47-1-233-239","DOIUrl":null,"url":null,"abstract":"Based on machine learning algorithms, a method for predicting risks in the European energy markets has been proposed. The work is aimed at developing intelligent risk management systems that utilize advanced artificial intelligence technologies for assessing and minimizing potential threats. Utilizing historical data and current market trends, a comprehensive approach to identifying price volatility and risk zones in the energy markets is presented. The study demonstrates how artificial intelligence can enhance the effectiveness of decisions made by managers in the energy markets and ensure more sustainable resource management in conditions of increasing uncertainty. The results show that the use of complex machine learning algorithms and data analysis can significantly improve the accuracy of risk prediction and contribute to the adoption of well-founded managerial decisions.","PeriodicalId":509753,"journal":{"name":"Optoelectronic Information-Power Technologies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent risk management systems in european energy markets\",\"authors\":\"O.A. Poplavskyi, O.I. Soroka, M.O. Litvin, A.V. Poplavskyi\",\"doi\":\"10.31649/1681-7893-2024-47-1-233-239\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on machine learning algorithms, a method for predicting risks in the European energy markets has been proposed. The work is aimed at developing intelligent risk management systems that utilize advanced artificial intelligence technologies for assessing and minimizing potential threats. Utilizing historical data and current market trends, a comprehensive approach to identifying price volatility and risk zones in the energy markets is presented. The study demonstrates how artificial intelligence can enhance the effectiveness of decisions made by managers in the energy markets and ensure more sustainable resource management in conditions of increasing uncertainty. The results show that the use of complex machine learning algorithms and data analysis can significantly improve the accuracy of risk prediction and contribute to the adoption of well-founded managerial decisions.\",\"PeriodicalId\":509753,\"journal\":{\"name\":\"Optoelectronic Information-Power Technologies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optoelectronic Information-Power Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31649/1681-7893-2024-47-1-233-239\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronic Information-Power Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31649/1681-7893-2024-47-1-233-239","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent risk management systems in european energy markets
Based on machine learning algorithms, a method for predicting risks in the European energy markets has been proposed. The work is aimed at developing intelligent risk management systems that utilize advanced artificial intelligence technologies for assessing and minimizing potential threats. Utilizing historical data and current market trends, a comprehensive approach to identifying price volatility and risk zones in the energy markets is presented. The study demonstrates how artificial intelligence can enhance the effectiveness of decisions made by managers in the energy markets and ensure more sustainable resource management in conditions of increasing uncertainty. The results show that the use of complex machine learning algorithms and data analysis can significantly improve the accuracy of risk prediction and contribute to the adoption of well-founded managerial decisions.