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titleType: Subtitle
title: Application of neural network modeling to predict the load of electric power systems
Creators: name: ÐÐµÐ»Ð¾Ð², ÐÐ»ÐµÐºÑÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð­Ð»ÐµÐºÑÑÐ¾ÑÐ½ÐµÑÐ³ÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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Ð³ÑÐ°ÑÐ¸Ðº ÑÐ»ÐµÐºÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸
ÐºÑÐ°ÑÐºÐ¾ÑÑÐ¾ÑÐ½Ð¾Ðµ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
forecasting methods
time series analysis
electrical load graph
short-term forecasting

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description: Topic: Â«Application of neural network modeling to predict the load of electric power systemsÂ». The volume of the final qualifying work is 74 pages, which contain 26 figures, 1 table. When writing, 21 sources were used. The object of study is electric power facilities of the Novosibirsk region. The subject of the study is a schedule of electricity consumption from the Novosibirsk Regional Dispatch Office. Key words: forecasting methods, time series analysis, recurrent neural networks, electrical load graph, short-term forecasting. Subject of the study: Improving the accuracy of forecasting power consumption of power systems to improve the operating efficiency of power plants. The purpose and objectives of the work is development of software using neural networks and machine learning to improve the quality of forecasting power consumption of power systems using the example of NSO. To achieve the goal of the study, the following tasks were formulated and solved: - Analysis of existing methods and approaches for forecasting load schedules in the short and medium term. - Development and comparison of two forecasting algorithms using a neural network and determining the best one based on the criterion of minimum error. - Assessment of the applicability of the developed software using the example of the NSO power system.