Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

13

Číslo periodika v rámci svazku

VOLUME 13

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

93735-93754

Kód UT WoS článku

001502494300015

EID výsledku v databázi Scopus

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