CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE 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...
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General Energy Institute of the National Academy of Sciences of Ukraine
2025
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| author | Verpeta, Vladyslav |
| author_facet | Verpeta, Vladyslav |
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{
"author": "Vladyslav Verpeta",
"institution": null
}
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| datestamp_date | 2026-07-18T12:57:49Z |
| description | 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. |
| doi_str_mv | 10.15407/srenergy2025.02.091 |
| first_indexed | 2026-03-24T02:03:33Z |
| format | Article |
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Системні дослідження в енергетиці. 2025. 2(82) 91
ISSN 2786-7102 (Online), ISSN 2786-7633 (Print)
https://doi.org/10.15407/srenergy2025.02.091
UDC 620.9+621.3+519.2
Vladyslav Verpeta, https://orcid.org/0009-0001-3572-0388
General Energy Institute of NAS of Ukraine, 172, Antonovycha St., Kyiv, 03150, Ukraine
e-mail: vladverpeta@gmail.com
_______________________________________________________________________________________
CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE
GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY
Abstract. 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.
Keywords: distributed generation, renewable energy sources, geographic information system, GIS, solar
power plant, wind power plant, meteorological data, forecasting, integration, energy system.
1. Introduction
Ukraine, like many other countries around the world, is actively moving towards increasing the share of
renewable energy sources, including solar energy, in the energy balance. However, one of the main challenges
for the successful integration of solar power plants into the grid is their dependence on weather conditions,
which can vary significantly throughout the year [1]. Given that Ukraine is located in a temperate climate zone
with a diverse weather pattern, ensuring reliable forecasting of electricity generation becomes critical for the
stable operation of the power system [2].
Meteorological data is a key factor in forecasting solar energy production. The use of accurate and
reliable data on insolation, air temperature, wind speed and other meteorological parameters allows optimising
the operation of solar power plants, predicting their efficiency, and preventing possible energy shortages. As
Ukraine faces the need to improve its energy security, this study is of particular relevance [3].
The purpose of this article is to analyse the available open sources of meteorological data in terms of
their accuracy and suitability for forecasting electricity generation by solar power plants in Ukraine. This study
is aimed at identifying the most effective data sources for use in power grid management systems, as well as
assessing possible problems associated with differences in forecasts from different sources.
There is a significant amount of literature on the impact of meteorological conditions on the efficiency
of solar power plants [4−7]. Previous studies have highlighted the importance of accurate meteorological data
for forecasting electricity production, analysing seasonal variations in insolation, and assessing long-term
trends. In particular, studies in different regions of the world have shown that the accuracy of forecasts
Системні дослідження в енергетиці. 2025. 2(82) 92
significantly depends on the quality of meteorological data [8]. In light of this, analysing open data sources
available for Ukraine is an important step in ensuring the reliability of forecasts.
The main question of this study is which open sources of meteorological data are most suitable for
forecasting electricity generation by solar power plants in Ukraine. It is also important to investigate whether
there are significant discrepancies between the forecasts obtained from different sources and what factors may
influence these discrepancies.
2. Methodology
This study used meteorological data from several open sources, including such geographic information
systems (GIS) as NASA POWER and Open Meteo. In addition, other available sources providing
meteorological information were considered, but unfortunately, these services either do not provide
information in the public domain at all or provide a very limited number and quality of parameters, which is
insufficient for full-fledged research [9, 10]. To better understand further data analysis and the differences in
their readings, let's look at how the above-mentioned services collect and analyse data.
2.1. NASA POWER
NASA POWER (Prediction Of Worldwide Energy Resources) builds a database from a variety of
sources, including satellite observations, meteorological models and ground stations. The main meteorological
data used by NASA POWER comes from satellites that provide global observations of the atmosphere, oceans
and land. In addition, NASA uses climate models to analyse the data and make forecasts [11].
Terra and Aqua satellites are equipped with spectroradiometers that measure the amount of solar
radiation reaching the Earth's surface. Insolation data are essential for estimating solar energy production [12].
MODIS (Moderate Resolution Imaging Spectroradiometer) is an instrument on board Terra and Aqua that
provides information on cloud cover, surface temperature and other meteorological parameters.
Global Circulation Models (GCMs) are used to predict large-scale climate change and atmospheric
behaviour on a global scale. These models analyse satellite data to create weather and climate forecasts.
MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) is a NASA model that provides
a retrospective analysis of the atmosphere and provides data for assessing past climate conditions. Data from
MERRA-2 allows tracking long-term trends in insolation, temperature, humidity and other parameters [13].
Access to the processed data is provided through an API, which allows you to obtain information on
insolation, temperature, wind speed and other parameters for any point on Earth with a resolution of 0.5.
2.2. Open Meteo
This is a modern platform that specialises in providing data on current weather conditions, short-term
forecasts and historical data. Their system is based on weather information models that combine information
from various sources, such as global and regional climate models, as well as data from ground-based weather
stations.
The Global Forecast System (GFS) is a model developed by the US National Weather Prediction Centre
that provides global forecasts of weather factors based on the analysis of atmospheric conditions. The GFS
generates forecasts every 6 hours, taking into account various parameters such as temperature, humidity, wind
speed and atmospheric pressure [14].
The ICON model (ICOsahedral Nonhydrostatic Model) is used for highly accurate regional forecasts in
Europe. This model was developed by the German Weather Service (DWD) and provides high-resolution
forecasts for local conditions [15].
Ground-based weather stations in this GIS collect information about actual weather conditions. These
stations are located all over the world and provide local data on temperature, humidity, precipitation and other
parameters that are important for short-term forecasts.
Data from different climate models are compared and combined to improve forecast accuracy. The Open
Meteo API provides accurate forecasts for different geographical regions with a resolution of up to 0.1° and
covers all major meteorological parameters such as temperature, humidity, wind speed, precipitation and
insolation [16].
Системні дослідження в енергетиці. 2025. 2(82) 93
3. Methods
Statistical methods of data processing were used to analyse the meteorological data. The data were
compared with each other to determine the degree of their discrepancy. Elements of regression analysis were
used to determine the correlation between various meteorological parameters and electricity generation.
All calculations were performed using R and Python software packages that provide a wide range of
tools for data processing and analysis [17].
The correlation coefficient between two samples X and Y with a normal distribution law was determined
by the formula [18]:
( )
( ) ( )
( ) ( )
( ) ( )
cov , x y y yx y
r
x y x y
− −
= =
M M M
D D
, (3.1)
where cov(x,y) is the operator of covariance of random variables x and t; ( )x and ( )y are the variances of x
and y, respectively.
This indicator allowed us to assess the degree of data consistency and identify possible problems with
data reliability. Additionally, a sensitivity analysis was conducted to determine the impact of individual
meteorological parameters on the overall electricity generation forecast.
4. Results
4.1. Insolation data analysis
It should be noted that measuring solar insolation is not a simple task. There are dozens of different
methods and instruments designed to make such measurements [19]. However, even when using the same
instruments and methodologies, the results may differ due to the influence of various factors, such as the
variability of atmospheric phenomena (humidity, wind speed, air purity) and the angle of inclination relative
to the sun, etc.8].
NASA Power generates about 10 different parameters that allow to estimate solar activity, while
Open Meteo, in turn, also has several parameters describing solar activity. For the comparative analysis, we
chose the "global horizontal insolation" parameter, since it is present in both services and is most often used
in research to predict solar energy generation. The angle of inclination is 0 degrees, i.e. parallel to the ground,
which is rarely used in practice, but in order to avoid possible additional errors, this option was chosen [20].
Figure 1 shows 3 curved lines corresponding to the average hourly solar insolation values for April 2021. This
time of year was chosen to investigate the difference between data sources in the most variable season - spring.
Data for other periods of the year will not be considered in this article. The results of the correlation analysis
are presented in Table 1.
Fig. 1. Comparison of NASA Power and Open Meteo data with actual insolation values for April 2021
Системні дослідження в енергетиці. 2025. 2(82) 94
Table 1. Correlation matrix of solar insolation data
NASA Real Open-Meteo
NASA 1 0.90 0.79
Real 0.90 1 0.64
Open-Meteo 0.79 0.64 1
The correlation analysis of the insolation data revealed significant relationships between NASA
POWER data, real on-site measurements, and Open Meteo data. The highest r value (0.90) between NASA
data and real measurements emphasises the high accuracy and reliability of NASA data for forecasting solar
energy generation in Ukraine [21]. This is especially important in a changing climate, where accurate insolation
forecasts can ensure stable operation of solar power plants and minimise the risk of energy shortages.
The discrepancy between NASA and Open Meteo data (r = 0.79) may be due to different model
resolutions and approaches to data processing. For example, NASA uses more detailed climate models that
take into account global climate processes, while Open Meteo can use simplified algorithms for more
immediate forecasting. Nevertheless, Open Meteo data remains useful, especially for short-term forecasts
when data processing speed is a critical factor.
The r-value between real measurements and Open Meteo (0.64) indicates that this data can also be used,
but with some caveats. For example, in conditions of high weather variability, the use of Open Meteo data may
require additional calibration based on local measurements. This will improve the accuracy of forecasts and
ensure more efficient management of energy resources.
Insolation analysis that takes into account data for a full day may not fully reflect the actual operating
conditions of solar power plants. Including nighttime hours or periods with low insolation, when there is little
or no power generation, can distort the results and reduce the accuracy of forecasts due to additional noise.
To achieve more accurate and practically meaningful results, it is important to consider only those
periods when insolation really affects generation. This will improve the quality of the analysis and allow for
more accurate forecasts. Therefore, considering filtered data that includes only active insolation hours is a
necessary step.
The filtered data covers the periods of active solar insolation between 10 am and 6 pm, when the sun is
at its highest position in the season selected for analysis.
After filtering the data, the r-value between NASA data and real measurements dropped to 0.87, as can
be seen in Table 2. This still indicates a high positive correlation, but slightly lower than the unfiltered data.
This may be due to the fact that the filtering reduced the impact of periods with lower solar radiation intensity.
Table 2. Correlation matrix of filtered solar insolation data
NASA Real Open-Meteo
NASA 1 0.87 0.76
Real 0.87 1 0.74
Open-Meteo 0.76 0.74 1
The correlation between NASA and Open Meteo data also dropped after filtering, to 0.76. Although this
still indicates a strong connection.
The correlation between the real measurements and Open Meteo is 0.74, which also indicates a
moderately high agreement, which is clearly visible in the graphical visualisation in Figure 2. This may indicate
that after filtering, the Open Meteo data reflects the real conditions a little better than it did before filtering.
Системні дослідження в енергетиці. 2025. 2(82) 95
Fig. 2. Comparison of NASA Power, Open Meteo data with actual insolation values for April 2021 from 10 am to 6 pm
The analysis of the filtered data showed that all three data sources continue to demonstrate a high level
of consistency, but with a slightly reduced correlation compared to the unfiltered data. This highlights the
importance of taking into account periods of active insolation for accurate solar power generation forecasting.
4.2. Analysing temperature data at a height of 2 metres
This review discusses the main meteorological parameters that have the greatest impact on the efficiency
of solar panels. The relationship between thermal effects and solar cell performance has been studied many
times and indicates a significant degradation of generation levels when the temperature goes beyond normal
conditions [22]. Therefore, ambient temperature plays a critical role in the overall efficiency of photovoltaic
systems [23].
Consider Fig. 3, which illustrates temperature changes at a height of 2 metres above the ground.
Fig. 3. Comparison of NASA Power, Open Meteo data with actual air temperature values above the ground for April
2021
The blue and green lines (NASA and Open Meteo data, respectively) show high agreement throughout
the period. This confirms the high correlation found in the correlation matrix (0.89), making these sources
reliable for temperature forecasting.
Системні дослідження в енергетиці. 2025. 2(82) 96
The orange line, representing the real measurements, also has a high level of agreement with NASA
POWER (0.89) and Open Meteo (0.78). Despite some differences, the real data basically follows the same
trends as the NASA and Open Meteo data.
While all three sources show similar overall trends, there are differences in short-term temperature
fluctuations at some locations. This may be the result of different temporal resolution of the data or different
methods of data processing. The correlation matrix is presented in Table 3.
Table 3. Correlation matrix of temperature data at a height of 2 metres
NASA Real Open-Meteo
NASA 1 0.89 0.89
Real 0.89 1 0.78
Open-Meteo 0.89 0.78 1
The analysis of NASA POWER data revealed a high correlation with both actual measurements and
Open Meteo data (0.89), which indicates a close correspondence between these sources in terms of temperature
at a height of 2 metres. This consistency makes both datasets reliable for temperature forecasting in the context
of solar power plant operations.
Although the correlation between real measurements and Open Meteo is slightly lower (0.78), it is still
high enough to be used effectively in forecasts. This confirms that both sources can be used to analyse
temperature conditions that affect electricity production. Despite the slight difference in correlation scores,
Open Meteo data is also a valuable tool for short-term temperature forecasts, especially in the context of solar
power plant management.
4.3. Analysing wind speed data at a height of 10 metres
Wind speed is a critical parameter for the efficient operation of solar power plants, as it affects the
cooling of the panels and, consequently, their efficiency. Wind can help to cool the panels, which increases
their performance, especially in high temperature conditions. It is also important to consider wind speed when
designing solar panel structures, as strong winds can lead to mechanical damage [4, 24].
Therefore, accurate forecasting of wind speeds at different heights is necessary to ensure stable operation of
solar power plants and to plan their operation [25]. Figure 4 shows a comparison of wind speed data at a height
of 10 metres from three different sources.
Fig. 4. Comparison of NASA Power, Open Meteo data with actual wind speed values at a height of 10 metres above the
ground for April 2021
Системні дослідження в енергетиці. 2025. 2(82) 97
Table 4. Correlation matrix of wind speed data at a height of 10 metres above the ground
NASA Real Open-Meteo
NASA 1 0.85 0.83
Real 0.85 1 0.79
Open-Meteo 0.83 0.79 1
The graph and results of the correlation analysis in Table 4 show that the data from NASA POWER and
Open Meteo have a high degree of consistency both with each other and with real wind speed measurements.
Since wind speed is a very variable quantity, it is necessary to use averages over time, but each source has
different methods and periods of data collection, which can explain the lowest correlation among the 3
parameters considered. This is because wind speed, although it affects the efficiency of the panels, has a more
cumulative effect and does not affect generation rates instantly. This allows us to consider these sources as
reliable for forecasting wind conditions.
5. Discussion
The study shows that the correlation of meteorological parameters between different data sources differs.
This can be explained by several reasons:
1. Methods of data collection and processing. Different sources use different approaches to collecting
and analysing information. For example, NASA POWER is based on satellite observations and global climate
models, which provides high accuracy on a global scale, but may be less accurate in regions with pronounced
local climate features.
2. Influence of local conditions. Topography, water bodies, forests, and other geographical features
can significantly affect parameters such as temperature and wind speed. Local factors can change even over
short distances, making global models less accurate for specific locations.
3. Time scales of forecasts. The accuracy of forecasts depends on their duration. Short-term forecasts
may be more accurate for parameters such as temperature and insolation, while long-term forecasts take into
account general climate trends that may affect accuracy at a particular point in time.
4. The different levels of correlation between the parameters underline the importance of using a
combination of different data sources to forecast electricity generation. Combining information from several
resources allows to compensate for the shortcomings of each of them and provide more accurate and reliable
forecasts.
Taking into account various meteorological parameters such as insolation, temperature and wind speed
allows us to more accurately predict the amount of electricity that will be generated by a solar power plant at
a particular time. This, in turn, helps to optimise the operation of the power system, reduce the risks of
electricity shortages or surpluses, and increase the overall efficiency of solar resources [26].
6. Conclusions
The study found that NASA POWER data is the most reliable for long-term forecasts due to its high
correlation with real measurements. This makes them indispensable for strategic planning of solar power
plants, especially when the accuracy of forecasts is crucial, for example, when estimating annual electricity
generation or planning large-scale projects.
Open Meteo, although showing a slightly lower correlation, has its advantages. Its higher resolution
allows it to be effectively used for short-term forecasts or situations where a quick assessment of
meteorological conditions at a particular time is required. This is especially useful for operational management
of stations when it is necessary to respond immediately to weather changes.
It is important to note that temperature parameters show the highest stability between both sources,
which allows you to expect high accuracy regardless of the service you choose. However, for other parameters,
such as wind speed, where there is more variability, differences in accuracy between NASA POWER and Open
Системні дослідження в енергетиці. 2025. 2(82) 98
Meteo should be taken into account. In particular, it is recommended to use NASA POWER data for
forecasting wind risks at solar power plants.
Based on the results obtained, it is advisable to combine data from both sources, adapting them to
specific tasks, to achieve maximum forecast accuracy. This approach will compensate for the shortcomings of
each resource and increase the overall reliability of forecasts. In situations where the priority is the speed of
response, you can rely on Open Meteo, while for strategic planning, NASA POWER is better suited.
Further research should focus on developing forecasting models that integrate data from different
sources to improve forecast accuracy, even when data availability or accuracy is limited. Regular validation
and comparison of these resources will help to identify discrepancies in a timely manner and adjust models for
long-term planning.
In addition, the development of adaptive algorithms that can automatically select the most accurate data
for each parameter will help to take into account regional and seasonal characteristics, increasing the reliability
of forecasts. Further research should also focus on the impact of additional meteorological parameters, such as
cloud cover and humidity, which can significantly improve power generation forecasting.
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Системні дослідження в енергетиці. 2025. 2(82) 100
КОРЕЛЯЦІЙНИЙ АНАЛІЗ ПАРАМЕТРІВ КЛІМАТИЧНИХ
ГЕОІНФОРМАЦІЙНИХ СИСТЕМ ДЛЯ ВІДНОВЛЮВАНОЇ
ЕНЕРГЕТИКИ
Владислав Верпета, https://orcid.org/0009-0001-3572-0388
Інститут загальної енергетики НАН України, вул. Антоновича, 172, Київ, 03150, Україна
e-mail: vladverpeta@gmail.com
Анотація. У статті досліджено технічні аспекти інтеграції розподіленої відновлюваної генерації,
зокрема сонячної енергії, в енергетичну систему України, яка перебуває на етапі масштабної
трансформації, з метою підвищення надійності, стійкості та ефективності. Розглянуто
актуальність переходу до відновлюваних джерел енергії в контексті глобальних екологічних
викликів та зобов’язань України щодо зменшення викидів парникових газів. Особливу увагу
приділено аналізу метеорологічних даних як ключового фактора для точного прогнозування
генерації електроенергії сонячними електростанціями. Основну частину дослідження
зосереджено на кореляційному аналізі даних відкритих кліматичних геоінформаційних систем
NASA POWER та Open Meteo. Ці ресурси надають доступ до широкого спектру даних, включаючи
параметри інсоляції, температури повітря та швидкості вітру, які є критично важливими для
моделювання та прогнозування роботи сонячних та вітрових електростанцій. Проведено
порівняння цих даних з даними метеостанцій, встановлених на діючій сонячній електростанції, що
дозволило оцінити точність та надійність даних з кожного джерела. Виявлено, що комбінування
даних із NASA POWER, відомого своєю високою загальною точністю, та Open Meteo, яке
характеризується більшою просторовою та часовою роздільною здатністю, призводить до
значного покращення точності прогнозів. Це особливо важливо в контексті оперативного
управління енергосистемою та планування навантажень. Зроблено висновок про необхідність
системного та міждисциплінарного підходу до вирішення поставлених завдань. Рекомендовано
впровадження сучасних методів прогнозування, що використовують алгоритми машинного
навчання та штучного інтелекту для обробки великих обсягів метеорологічних даних. Наголошено
на важливості розвитку національної інфраструктури збору та аналізу даних, що дозволить
підвищити надійність та ефективність роботи енергетичної системи в умовах зростаючої
частки відновлюваної генерації.
Ключові слова: розподілена генерація, відновлювані джерела енергії, геоінформаційна система,
ГІС, сонячна електростанція, вітрова електростанція, метеорологічні дані, прогнозування,
інтеграція, енергетична система.
Надійшла до редколегії: 02.10.2024
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| id | systemreorg-article-899 |
| institution | System Research in Energy |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:23:38Z |
| publishDate | 2025 |
| publisher | General Energy Institute of the National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | systemreorg/ca/71e227e727cc8e8544a0cdb8078ffbca.pdf |
| spelling | systemreorg-article-8992026-07-18T12:57:49Z CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY Кореляційний аналіз параметрів кліматичних геоінформаційних систем для відновлюваної енергетики Verpeta, Vladyslav distributed generation, renewable energy sources, geographic information system, GIS, solar power plant, wind power plant, meteorological data, forecasting, integration, energy system. розподілена генерація, відновлювані джерела енергії, геоінформаційна система, ГІС, сонячна електростанція, вітрова електростанція, метеорологічні дані, прогнозування, інтеграція, енергетична система. 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. У статті досліджено технічні аспекти інтеграції розподіленої відновлюваної генерації, зокрема сонячної енергії, в енергетичну систему України, яка перебуває на етапі масштабної трансформації, з метою підвищення надійності, стійкості та ефективності. Розглянуто актуальність переходу до відновлюваних джерел енергії в контексті глобальних екологічних викликів та зобов’язань України щодо зменшення викидів парникових газів. Особливу увагу приділено аналізу метеорологічних даних як ключового фактора для точного прогнозування генерації електроенергії сонячними електростанціями. Основну частину дослідження зосереджено на кореляційному аналізі даних відкритих кліматичних геоінформаційних систем NASA POWER та Open Meteo. Ці ресурси надають доступ до широкого спектру даних, включаючи параметри інсоляції, температури повітря та швидкості вітру, які є критично важливими для моделювання та прогнозування роботи сонячних та вітрових електростанцій. Проведено порівняння цих даних з даними метеостанцій, встановлених на діючій сонячній електростанції, що дозволило оцінити точність та надійність даних з кожного джерела. Виявлено, що комбінування даних із NASA POWER, відомого своєю високою загальною точністю, та Open Meteo, яке характеризується більшою просторовою та часовою роздільною здатністю, призводить до значного покращення точності прогнозів. Це особливо важливо в контексті оперативного управління енергосистемою та планування навантажень. Зроблено висновок про необхідність системного та міждисциплінарного підходу до вирішення поставлених завдань. Рекомендовано впровадження сучасних методів прогнозування, що використовують алгоритми машинного навчання та штучного інтелекту для обробки великих обсягів метеорологічних даних. Наголошено на важливості розвитку національної інфраструктури збору та аналізу даних, що дозволить підвищити надійність та ефективність роботи енергетичної системи в умовах зростаючої частки відновлюваної генерації. General Energy Institute of the National Academy of Sciences of Ukraine 2025-05-07 Article Article application/pdf https://systemre.org/index.php/journal/article/view/899 10.15407/srenergy2025.02.091 System Research in Energy; No. 2 (82) (2025): System Research in Energy; 91-100 Системні дослідження в енергетиці; № 2 (82) (2025): Системні дослідження в енергетиці; 91-100 2786-7102 2786-7633 en https://systemre.org/index.php/journal/article/view/899/804 Copyright (c) 2025 Vladyslav Verpeta https://creativecommons.org/publicdomain/zero/1.0 |
| spellingShingle | distributed generation renewable energy sources geographic information system GIS solar power plant wind power plant meteorological data forecasting integration energy system. Verpeta, Vladyslav CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY |
| title | CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY |
| title_alt | Кореляційний аналіз параметрів кліматичних геоінформаційних систем для відновлюваної енергетики |
| title_full | CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY |
| title_fullStr | CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY |
| title_full_unstemmed | CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY |
| title_short | CORRELATION ANALYSIS OF PARAMETERS OF CLIMATE GEOINFORMATION SYSTEMS FOR RENEWABLE ENERGY |
| title_sort | correlation analysis of parameters of climate geoinformation systems for renewable energy |
| topic | distributed generation renewable energy sources geographic information system GIS solar power plant wind power plant meteorological data forecasting integration energy system. |
| topic_facet | distributed generation renewable energy sources geographic information system GIS solar power plant wind power plant meteorological data forecasting integration energy system. розподілена генерація відновлювані джерела енергії геоінформаційна система ГІС сонячна електростанція вітрова електростанція метеорологічні дані прогнозування інтеграція енергетична система. |
| url | https://systemre.org/index.php/journal/article/view/899 |
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