Jain, N.Naik, D.2026-02-0620242024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024, 2024, Vol., , p. -https://doi.org/10.1109/ICCCNT61001.2024.10726181https://idr.nitk.ac.in/handle/123456789/28821Maintaining equilibrium between generation and load is crucial for maximizing economic scheduling in smart grids. As solar energy forecasting gains importance due to its sporadic nature and climatic dependencies, this study leverages advanced machine learning and deep learning models for accurate prediction. Specifically, we use XGBoost and Long Short-Term Memory (LSTM) models to analyze data from two solar installations in India over a 34-day period. Our approach enhances the efficiency and reliability of solar energy utilization in smart grids. Evaluated over a 3-day test period, the LSTM model achieved an RMSE of approximately 2870 kW, a 22% improvement over a baseline model with an RMSE of 3699 kW. These results highlight the potential of machine learning and deep learning to improve solar power forecasting accuracy, thereby facilitating more effective energy management in smart grids. © 2024 IEEE.Deep LearningForecastingLSTMMachine LearningXGBoost