A Comparative Review on Energy Power Prediction using Light Gradient Boosting Machine and Hybrid of Extreme Gradient Boosting

The global transition toward renewable energy sources has necessitated the development of highly accurate power prediction models to ensure grid stability and efficient energy management. Renewable energy, particularly solar and wind, is inherently intermittent and volatile, depending heavily on fluctuating meteorological conditions. This research presents a comprehensive comparative review and analysis of energy power prediction using the Light Gradient Boosting Machine (LightGBM) and a hybrid framework based on Extreme Gradient Boosting (XGBoost). The study explores the technical architectures of these gradient-boosting decision tree (GBDT) variants, evaluating their performance across diverse datasets. LightGBM is recognized for its computational efficiency and histogram-based learning, while the hybrid XGBoost approach aims to enhance predictive robustness through ensemble stacking and hyperparameter optimization. This paper details the mathematical foundations of both models, the importance of feature engineering in energy data, and the metrics used for performance evaluation. The findings suggest that while LightGBM offers superior training speed for large-scale energy datasets, the hybrid XGBoost model often achieves higher generalization accuracy by mitigating overfitting through advanced regularization. The research concludes with a roadmap for integrating these models into real-time smart grid monitoring systems to facilitate a more reliable and sustainable energy future.