Researchers at North Carolina State University have demonstrated techniques that improved day-ahead solar power forecasting by up to 13% compared with the most consistently performing individual model.The findings were published in the Journal of Cleaner Production in a paper titled “A Hybrid Machine Learning Framework for Enhanced Day-Ahead Solar Forecasting in Large Scale Systems.”The research showed that combining multiple machine-learning models can yield more robust predictions and enhance the reliability of solar integration into power systems.
