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September 5, 2025EnergiesOpen Access

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

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Authors

LZLianglin ZouHQHongyang QuanPTPing Tang

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Overview

This framework enhances solar power forecasting accuracy, leveraging ensemble learning and improving dynamic weighting strategies.

Key Points

  • The proposed framework significantly enhances prediction robustness for photovoltaic power output.
  • Using a three-level optimization architecture, the method integrates model strengths and geographical variables.
  • The heterogeneous model pool is constructed through mutual information and hierarchical clustering techniques.
  • Performance was validated with data from a 75 MW photovoltaic power plant, signaling practical application.

Cite This Study

Zou et al. (2025) studied this question.

synapsesocial.com/papers/68c23a2cb210217d6477fee9https://doi.org/10.3390/en18174644
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  1. 1Enhancing Photovoltaic Power Forecasting via Dual Signal Decomposition and an Optimized Hybrid Deep Learning Framework2025
  2. 2Forecasting Solar Photovoltaic Power Generation: A Machine Learning Time Series Model Approach2025
  3. 3A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model2025 · 12 citations
  4. 4A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting2025 · 2 citations
  5. 5A Hybrid Decomposition and Deep Learning Model for Photovoltaic Power Forecasting Under Variable Meteorological Conditions2025