Кореляційний аналіз параметрів кліматичних геоінформаційних систем для відновлюваної енергетики

• Дата: 2025
• Автор: Verpeta, Vladyslav
• Формат: Стаття
• Мова: English
• Опубліковано: General Energy Institute of the National Academy of Sciences of Ukraine 2025
• Теми: розподілена генерація; відновлювані джерела енергії; геоінформаційна система; ГІС; сонячна електростанція; вітрова електростанція; метеорологічні дані; прогнозування; інтеграція; енергетична система.
• Онлайн доступ: https://systemre.org/index.php/journal/article/view/899
• Назва журналу: System Research in Energy

The paper examines the technical aspects of the integration of distributed renewable generation, in particular solar energy, into the energy system of Ukraine, which is undergoing a large-scale transformation with the aim of increasing reliability, sustainability and efficiency. The relevance of the transition to renewable energy sources in the context of global environmental challenges and Ukraine's obligations to reduce greenhouse gas emissions is considered. Special attention is paid to the analysis of meteorological data as a key factor for accurate forecasting of electricity generation by solar power plants. The main part of the research is focused on the correlation analysis of data from NASA POWER and Open Meteo open climate geoinformation systems. These resources provide access to a wide range of data, including parameters of insolation, air temperature and wind speed, which are critical for modelling and forecasting the operation of solar and wind farms. A comparison of these data with data obtained from weather stations installed at an operating solar power plant was carried out, which made it possible to assess the accuracy and reliability of data from each source. Combining data from NASA POWER, known for its high overall accuracy, and Open Meteo, characterised by higher spatial and temporal resolution, has been found to significantly improve forecast accuracy. This is especially important in the context of operational power system management and load planning. A conclusion was made about the need for a systematic and interdisciplinary approach to solving the tasks. The implementation of modern forecasting methods using machine learning and artificial intelligence algorithms for processing large volumes of meteorological data is recommended. The importance of the development of the national data collection and analysis infrastructure is emphasised, which will increase the reliability and efficiency of the energy system in the face of a growing share of renewable generation.