Shining a Light on Solar Power Forecasting: Machine Learning Techniques for Unprecedented Accuracy

Accurate solar power forecasting is critical for efficient energy management and grid integration. This study looks into how to forecast solar power using weather forecast data and machine learning methods. Several machine learning models have been proposed to forecast solar power, including Artificial Neural Networks, Random Forests, Support Vector Regressions, Gradient Boosting, and Hybrid AI models. However, the proposed Deep Belief Networks (DBNs) model has several advantages over conventional machine learning models, including its capacity to learn intricate patterns and relationships in the data, lowering the requirement for manual feature engineering, and scalability for both short- and long-term forecasting. The comparison table and accuracy metrics show that the suggested model performs better in terms of accuracy than conventional models and takes less time to run. The proposed model provides valuable insights into the optimal design and implementation of solar power forecasting systems, supporting the transition to a sustainable energy future.