This study contributes to the growing body of research on solar energy forecasting by:—Demonstrating the application and comparative performance of five machine learning models in
By investigating the most recent literature, this review identifies critical research gaps and suggests future directions for enhancing forecasting models, including improving model
Abstract: Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy
The study compared three advanced prediction algorithms — support vector regression, random forest, and neural networks — providing insights into improving the accuracy of short-term
With the aim of enhancing the accuracy and reliability of forecasts, this study presents a comprehensive comparative analysis of eight state-of-the-art Deep Learning (DL)
The data gathered from the solar photovoltaic system is initially visualized using a data analysis tool. Second, by employing multiple statistical indices to predict values from a time-series
Hence, this study proposes the Extreme Gradient Boosting regression-based Solar Photovoltaic Power Generation Prediction (XGB-SPPGP) model to predict and classify the usage of
The factors influencing solar energy power generation include geographic location, solar radiation, weather conditions, and solar panel performance. Solar energy forecasting is performed
In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost
solar power systems are efficient and cost-effective. Accurate predictions can help power companies better manage their solar power plants, reduce en rgy waste, and ensure that energy supply meets
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