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November 28, 2025EnergiesOpen Access

Enhancing Photovoltaic Power Forecasting via Dual Signal Decomposition and an Optimized Hybrid Deep Learning Framework

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Authors

WWWenjie WangMZMin ZhangZZZhirong Zhang

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Overview

Analysis demonstrates reduced mean absolute error in photovoltaic power generation forecasting using a metaheuristic algorithm and deep learning techniques.

Key Points

  • The model achieved a Mean Absolute Error of 0.3396, indicating significant accuracy improvements.
  • Assessment using temporal convolutional operations and an advanced metaheuristic algorithm enhanced forecasting performance.
  • Leveraging K-means clustering facilitates the management of complex data, improving model predictions.
  • This framework highlights the effectiveness of hybrid deep learning in photovoltaic power forecasting, requiring further validation in diverse datasets.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/6928f113a65b730b9ea79fbchttps://doi.org/10.3390/en18236159
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Also Consider

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  1. 1A Hybrid Decomposition and Deep Learning Model for Photovoltaic Power Forecasting Under Variable Meteorological Conditions2025
  2. 2A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting2025 · 2 citations
  3. 3A hybrid framework for short-term solar power prediction using signal decomposition and attention-based network2026
  4. 4Short-term photovoltaic power prediction based on dual decomposition with TCN-Informer-xLSTM2025
  5. 5A Wavelet–Attention–Convolution Hybrid Deep Learning Model for Accurate Short-Term Photovoltaic Power Forecasting2025