LightGBM Medium-Term Photovoltaic Power Prediction Integrating Meteorological Features and Historical Data

This paper proposes a Light Gradient Boosting Machine (LightGBM) model for medium-term photovoltaic (PV) power forecasting by integrating meteorological features with historical generation data. This approach addresses prediction biases that often arise when relying solely on a single meteorological data source. Historical power output and meteorological variables (irradiance, temperature, humidity, etc.) were collected from a PV station and preprocessed through data cleaning, standardization, and temporal alignment to construct a multivariate prediction framework. A comprehensive feature set was then built, including meteorological, temporal, interaction, and lag features. Feature importance analysis and Recursive Feature Elimination (RFE) were employed for input optimization, while feature-layer concatenation was applied for data fusion. Finally, the LightGBM (Version 2.3.1) framework, combined with Bayesian optimization and time-series cross-validation, was used to enhance generalization and predictive robustness. Experimental results confirm that the model achieved an MAE of 37.49, RMSE of 64.67, and R2 of 0.89. The model effectively captured high-dimensional nonlinear relationships, thereby improving the accuracy of medium-term photovoltaic forecasts and providing reliable decision support for power system scheduling and renewable energy integration.