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November 11, 2025Scientific ReportsOpen Access

A novel hybrid model integrating CEEMDAN decomposition, dispersion entropy and LSTM for photovoltaic power forecasting and anomaly detection

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Authors

ZQZi-qi QiuJYJiarong YeJLJia-hui Lu

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Overview

The analysis identifies line faults and partial shading in distributed photovoltaic systems, suggesting improved forecasting accuracy via a hybrid approach.

Key Points

  • The hybrid framework effectively enhances photovoltaic power forecasting and anomaly detection.
  • It identifies line faults and partial shading, improving predictive accuracy in distributed photovoltaic systems.
  • The analysis integrates CEEMDAN decomposition with dispersion entropy and LSTM for dynamic feature fusion.
  • This approach highlights the potential for operational improvements in distributed photovoltaic systems.

Cite This Study

Qiu et al. (2025) studied this question.

synapsesocial.com/papers/69252e96c0ce034ddc3561e4https://doi.org/10.1038/s41598-025-23305-3
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A CEEMDAN-CNN-BiLSTM-SDQN Framework for Photovoltaic Power Forecasting: Integrating Multi-Scale Decomposition with Adaptive Reinforcement Learning Compensation2026
  2. 2Enhancing Photovoltaic Power Forecasting via Dual Signal Decomposition and an Optimized Hybrid Deep Learning Framework2025
  3. 3Enhancing Photovoltaic Power Forecasting via Dual Signal Decomposition and an Optimized Hybrid Deep Learning Framework2025 · 2 citations
  4. 4Enhanced multi-horizon photovoltaic power forecasting: A novel approach integrating ICEEMDAN decomposition with hierarchical frequency neural networks2025
  5. 5Short-Term Photovoltaic Power Forecasting Based on ICEEMDAN-TCN-BiLSTM-MHA2025 · 7 citations