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August 25, 2025MathematicsOpen Access

A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting

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Authors

CXChuan XiangXLXiang LiuWLWei Liu

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Overview

Cascaded data-driven forecasting improves accuracy and robustness in photovoltaic power output, suggesting better system stability.

Key Points

  • The proposed approach achieves a forecasting accuracy of 97.986%, indicating significant improvements over traditional methods.
  • Enhanced DAE effectively extracts key weather factors, eliminating redundancy and strengthening feature representation.
  • Refined FCM clusters complex PV output scenarios, evidenced by improved Silhouette Coefficient and Calinski–Harabasz Index metrics.
  • LSTM–TPA model captures fine-grained temporal features, allowing adaptive adjustment of forecasting weights for better predictions.

Cite This Study

Xiang et al. (2025) studied this question.

synapsesocial.com/papers/68af7df87567bf4f94ff4fa1https://doi.org/10.3390/math13172728
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  1. 1Enhancing Photovoltaic Power Forecasting via Dual Signal Decomposition and an Optimized Hybrid Deep Learning Framework2025
  2. 2A Hybrid Decomposition and Deep Learning Model for Photovoltaic Power Forecasting Under Variable Meteorological Conditions2025
  3. 3A Photovoltaic Power Prediction Framework Based on Multi-Stage Ensemble Learning2025
  4. 4A photovoltaic power forecasting method based on the LSTM-XGBoost-EEDA-SO model2025
  5. 5Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables2025