alternativeАльтернативная энергетика и экология (ISJAEE)Alternative Energy and Ecology (ISJAEE)1608-8298Международный издательский дом научной периодики "Спейс10.15518/isjaee.2024.02.086-099alternative-2358Research ArticleI. ВОЗОБНОВЛЯЕМАЯ ЭНЕРГЕТИКА. 8. Энергокомплексы на основе ВИЭI. RENEWABLE ENERGY. 8. Energy of biomassСреднесрочное прогнозирование естественного притока к створу ГЭС и корректировка номинальной мощности с помощью применения технологий водородной энергетикиMedium-term forecasting of natural inflow HPPМитрофановС. В.MitrofanovS. V.

Митрофанов Сергей Владимирович, к.т.н., доцент, научный сотрудник отделения интеллектуальных систем инженерной школы «Интеллектуальные энергетические системы», Томского политехнического университета. Доцент кафедры систем электроснабжения предприятий Новосибирского государственного технического университета

пр-т К. Маркса, 20, г. Новосибирск, 630073

тел: 8 (383) 346-08-43, факс: (383) 346-02-09

пр-т Ленина, 30, г. Томск, 634050

тел: +7 (3822) 60-63-3, факс: +7 (3822) 60-64-44

Mitrofanov Sergey V., Ph.d. researcher at department of intelligent systems of the engineering school «Intelligent Energy Systems» (ES IES ISD), Tomsk Polytechnic University. Associate Professor, Department of Power Supply Systems, Novosibirsk State Technical University

K. Marx Avenue, 20, Novosibirsk, 630073

phone: 8 (383) 346-08-43, fax: (383) 346-02-09

Lenin Avenue, 30, Tomsk, 634050

phone: +7 (3822) 60-63-3, fax: +7 (3822) 60-64-44

mitrofan_serg@mail.ruСидороваА. В.SidorovaA. V.

Сидорова Алена Владимировна, научный сотрудник Межкафедральной научно-исследовательской лаборатории обработки, анализа и представления данных в электроэнергетических системах

пр-т К. Маркса, 20, г. Новосибирск, 630073

тел: 8 (383) 346-08-43, факс: (383) 346-02-09

Sidorova Alena V., researcher at the Interdepartmental Research Laboratory for Processing, Analysis and Presentation of Data in Electric Power Systems 

K. Marx Avenue, 20, Novosibirsk, 630073

phone: 8 (383) 346-08-43, fax: (383) 346-02-09

РусинаА. Г.RusinaA. G.

Русина Анастасия Георгиевна, д.т.н., декан факультета энергетики, заведующий кафедрой Электрических станций НГТУ НЭТИ, эксперт РАН, член IEEE

пр-т К. Маркса, 20, г. Новосибирск, 630073

тел: 8 (383) 346-08-43, факс: (383) 346-02-09

Rusina Anastasia G., Doc. of Sc., Dean of the Faculty of Energy, Head of the Power Plants Department at Novosibirsk State Technical University (NSTU NETI), expert at the RAS, member of IEEE

K. Marx Avenue, 20, Novosibirsk, 630073

phone: 8 (383) 346-08-43, fax: (383) 346-02-09

Новосибирский Государственный Технический Университет; Томский политехнический университет, ИШИнЭС ОИСРоссияNovosibirsk State Technical University; Tomsk Polytechnic University, ES IES ISDRussian FederationНовосибирский Государственный Технический УниверситетРоссияNovosibirsk State Technical UniversityRussian Federation202415052024028699Copyright © Международный издательский дом научной периодики "Спейс, 20232023Международный издательский дом научной периодики "СпейсМеждународный издательский дом научной периодики "Спейсhttps://www.isjaee.com/jour/about/submissions#copyrightNoticehttps://www.isjaee.com/jour/article/view/2358

В статье содержится обоснование важности и необходимости прогнозирования естественного притока к створу ГЭС, а также изложение основных принципов, затрудняющих управление режимами ГЭС без моделей прогнозирования. Отдельное внимание уделяется описанию используемых методов прогнозирования, оценки их точности, достоинств и недостатков. Создание модели и опробование алгоритмов прогнозирования естественного притока к створу ГЭС реализовано методами машинного обучения в Python с использованием библиотеки scikit-learn. В качестве объекта исследования выбрана Новосибирская ГЭС.

В ходе сбора и анализа данных была создана выборка из метеорологических сведений за 3287 дней со значениями температуры воздуха, давления, осадков, влажности и естественного притока к створу ГЭС. Прогнозирование притока к створу ГЭС реализовано на моделях линейной регрессии, полиноминальной регрессии второй степени, ближайших соседей, деревьев решений и случайного леса решений.

Полученные результаты оценки критериев точности MAPE, RMSE, R2 и MSE для каждой из рассматриваемых моделей прогнозов естественного притока к створу ГЭС показали, что реализация наиболее точного среднесрочного прогноза притока к створу ГЭС была достигнута моделью на основе случайного леса деревьев решений.

The article contains a rationale for the importance and necessity of forecasting the natural inflow HPP, as well as a statement of the basic principles that complicate the management of HPP modes without forecasting models. Special attention is paid to the description of the forecasting methods used, assessment of their accuracy, advantages and disadvantages. The creation of a model and testing of algorithms for predicting natural inflow HPP was implemented using machine learning methods in Python and scikit-learn library. The Novosibirsk HPP was chosen as the object of the study. During the collection and analysis of data, a sample was created from meteorological information for 3287 days with values of air temperature, pressure, precipitation, humidity and natural inflow HPP. Forecasting of inflow HPP is implemented using linear regression, second-degree polynomial regression, nearest neighbors, decision trees and random decision forest models.

The obtained results of assessing the accuracy criteria MAPE, RMSE, R2 and MSE for each of the considered models for forecasting natural inflow HPP showed that a model based on a random forest of decision trees achieved the implementation of the most accurate medium-term forecast of inflow HPP.

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The authors declare that there are no conflicts of interest present.