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

Yu Yang, Soon-Hyung Lee, Yong-Sung Choi and Kyung-Min Lee ()
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Yu Yang: Department of Electrical Engineering, Dongshin University, Naju 58245, Republic of Korea
Soon-Hyung Lee: Department of Electrical Engineering, Dongshin University, Naju 58245, Republic of Korea
Yong-Sung Choi: Department of Electrical Engineering, Dongshin University, Naju 58245, Republic of Korea
Kyung-Min Lee: Department of Electrical Engineering, Dongshin University, Naju 58245, Republic of Korea

Energies, 2025, vol. 18, issue 20, 1-12

Abstract: 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 R 2 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.

Keywords: medium-term photovoltaic power forecasting; LightGBM; multivariate; meteorological data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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