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November 14, 2025PLoS ONEOpen Access

Enhanced multi-horizon photovoltaic power forecasting: A novel approach integrating ICEEMDAN decomposition with hierarchical frequency neural networks

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Authors

HYHan Yao-pengLCLi, ChenxiCSChen Siqi

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Overview

Hybrid model improves accuracy in solar PV power generation forecasts using quantile regression and enhances statistical stability with uncertainty analysis.

Key Points

  • The hybrid model excels in achieving high accuracy for solar PV power generation forecasts under various conditions, confirming its robustness.
  • Quantile regression was employed to generate probabilistic prediction intervals, leading to precise forecasting outcomes.
  • This framework integrates ICEEMDAN decomposition with hierarchical frequency neural networks, allowing for advanced temporal dynamics capture.
  • Results showed significant improvements in forecasting accuracy, indicating the model's potential impact on power plant operations and planning.

Cite This Study

Yao-peng et al. (2025) studied this question.

synapsesocial.com/papers/692519b4c0ce034ddc3543f5https://doi.org/10.1371/journal.pone.0334828
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  4. 4Short-Term Photovoltaic Power Forecasting Based on ICEEMDAN-TCN-BiLSTM-MHA2025 · 7 citations
  5. 5A novel hybrid model integrating CEEMDAN decomposition, dispersion entropy and LSTM for photovoltaic power forecasting and anomaly detection2025