Improved Prediction of Photovoltaic Power based on LightGBM and Multi-source Meteorological Data

Predicting photovoltaic (PV) power is crucial for optimizing grid operations, achieving efficient energy management, and promoting the widespread adoption of renewable energy sources. This study introduces a PV power prediction model based on the LightGBM algorithm, enhanced by integrating multi-source meteorological data and feature construction methods. Experiments demonstrate improved accuracy with the incorporation of these newly constructed features. The proposed method provides a robust approach for distributed PV power prediction, offering significant economic and operational benefits to renewable energy generation.