A Photovoltaic Power Prediction Framework Based on Multi-Stage Ensemble Learning

Lianglin Zou, Hongyang Quan, Ping Tang, Shuai Zhang, Xiaoshi Xu and Jifeng Song ()
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Lianglin Zou: School of New Energy, North China Electric Power University, Beijing 102206, China
Hongyang Quan: School of New Energy, North China Electric Power University, Beijing 102206, China
Ping Tang: School of New Energy, North China Electric Power University, Beijing 102206, China
Shuai Zhang: School of New Energy, North China Electric Power University, Beijing 102206, China
Xiaoshi Xu: School of New Energy, North China Electric Power University, Beijing 102206, China
Jifeng Song: Institute of Energy Power Innovation, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 17, 1-19

Abstract: With the significant increase in solar power generation’s proportion in power systems, the uncertainty of its power output poses increasingly severe challenges to grid operation. In recent years, solar forecasting models have achieved remarkable progress, with various developed models each exhibiting distinct advantages and characteristics. To address complex and variable geographical and meteorological conditions, it is necessary to adopt a multi-model fusion approach to leverage the strengths and adaptability of individual models. This paper proposes a photovoltaic power prediction framework based on multi-stage ensemble learning, which enhances prediction robustness by integrating the complementary advantages of heterogeneous models. The framework employs a three-level optimization architecture: first, a recursive feature elimination (RFE) algorithm based on LightGBM–XGBoost–MLP weighted scoring is used to screen high-discriminative features; second, mutual information and hierarchical clustering are utilized to construct a heterogeneous model pool, enabling competitive intra-group and complementary inter-group model selection; finally, the traditional static weighting strategy is improved by concatenating multi-model prediction results with real-time meteorological data to establish a time-period-based dynamic weight optimization module. The performance of the proposed framework was validated across multiple dimensions—including feature selection, model screening, dynamic integration, and comprehensive performance—using measured data from a 75 MW photovoltaic power plant in Inner Mongolia and the open-source dataset PVOD.

Keywords: photovoltaic power forecasting; multi-model fusion; dynamic weighted voting; ensemble learning (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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