
В работе рассматриваются вопросы повышения точности прогнозирования генерации фотоэлектрических станций на открытых метеорологических данных с использованием методов машинного обучения, и алгоритма предварительной обработки исходных данных.
This work addresses the issues of improving the accuracy of forecasting the generation of photovoltaic power plants based on open meteorological data using machine learning methods and a preprocessing algorithm for the initial data.
RENEWABLE ENERGY SOURCES, АНСАМБЛЕВЫЕ АЛГОРИТМЫ, МАГИСТЕРСКАЯ ДИССЕРТАЦИЯ, MASTER'S THESIS, ВОЗОБНОВЛЯЕМЫЕ ИСТОЧНИКИ ЭНЕРГИИ, GENERATION FORECASTING, MACHINE LEARNING, ПОРГНОЗИРОВАНИЕ ГЕНЕРАЦИИ, МАШИННОЕ ОБУЧЕНИЕ, SHORT-TERM FORECASTING, ENSEMBLE ALGORITHMS, КРАТКОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ
RENEWABLE ENERGY SOURCES, АНСАМБЛЕВЫЕ АЛГОРИТМЫ, МАГИСТЕРСКАЯ ДИССЕРТАЦИЯ, MASTER'S THESIS, ВОЗОБНОВЛЯЕМЫЕ ИСТОЧНИКИ ЭНЕРГИИ, GENERATION FORECASTING, MACHINE LEARNING, ПОРГНОЗИРОВАНИЕ ГЕНЕРАЦИИ, МАШИННОЕ ОБУЧЕНИЕ, SHORT-TERM FORECASTING, ENSEMBLE ALGORITHMS, КРАТКОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ
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