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August 18, 2025Scientific ReportsOpen Access

A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model

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Authors

YXYing XuXJXinrong JiZZZhengyang Zhu

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Overview

The hybrid LSTM-XGBoost framework reduces forecasting errors in photovoltaic systems, indicating enhanced optimization techniques.

Key Points

  • The proposed method significantly reduces forecasting errors compared to traditional benchmarks.
  • Key meteorological factors are selected using Pearson correlation to refine input data for improved accuracy.
  • Hybrid forecasting employs parallel XGBoost and LSTM models to capture both global trends and temporal changes.
  • Dynamic weight optimization through the Snake Optimization algorithm enhances forecasting precision and adaptability.

Cite This Study

Xu et al. (2025) studied this question.

synapsesocial.com/papers/68af2ef6cf1dd9ea359e7378https://doi.org/10.1038/s41598-025-16368-9
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  1. 1Enhancing Photovoltaic Power Forecasting via Dual Signal Decomposition and an Optimized Hybrid Deep Learning Framework2025
  2. 2A Study on Novel Solar Power Forecasting using an XGB-LiGBM-RF Hybrid Model and the L-BFGS-B Optimization Algorithm2025
  3. 3A Photovoltaic Power Prediction Framework Based on Multi-Stage Ensemble Learning2025
  4. 4A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting2025 · 2 citations
  5. 5Optimized solar power forecasting: A multi-decomposition framework using VMD and swarm techniques2025