Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10257984" target="_blank" >RIV/61989100:27730/25:10257984 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11015947" target="_blank" >https://ieeexplore.ieee.org/document/11015947</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3574093" target="_blank" >10.1109/ACCESS.2025.3574093</a>
Alternative languages
Result language
angličtina
Original language name
Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability
Original language description
The increasing integration of solar photovoltaic (PV) systems into modern energy grids presents significant challenges due to the intermittent and weather-dependent nature of solar energy generation. Accurate short-term forecasting is essential to ensure grid stability and optimize energy resource allocation. This study proposes a comprehensive data-driven framework for solar energy forecasting using multiple machine learning (ML) techniques, including Multiple Linear Regression, Ridge, Lasso, Decision Tree Regression, Support Vector Regression, and ensemble-based models such as Random Forest, AdaBoost, Bagging, and Gradient Boosting Regressors. The framework incorporates advanced feature engineering using high-resolution meteorological and solar geometric parameters-such as relative humidity, temperature, cloud cover, zenith angle, azimuth, and angle of incidence-to enhance model accuracy. Historical solar power and weather datasets were used to train and evaluate the models across multiple performance metrics. Among the models, the Gradient Boosting Regressor demonstrated the best performance, achieving an R-2 of 0.827, RMSE of 399.44, and MAE of 253.62, marking a significant improvement over baseline models. The study also evaluates model robustness and discusses feature relevance, hyperparameter optimization strategies, and deployment considerations for real-time grid operations. These findings provide practical insights for stakeholders aiming to implement intelligent solar forecasting systems in smart grid environments, thereby contributing to enhanced energy management and grid resilience.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
13
Issue of the periodical within the volume
VOLUME 13
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
93735-93754
UT code for WoS article
001502494300015
EID of the result in the Scopus database
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