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<p>In the face of escalating global energy crises and pressing challenges of environmental pollution, the imperative for sustainable energy solutions has never been more pronounced. Photovoltaic (PV) power generation is recognized as a cornerstone in transition towards a clean energy paradigm. This study introduces a groundbreaking short-term PV power forecasting methodology based on teacher forcing (TF) integrated with bi-directional gated recurrent unit (BiGRU). Firstly, the chaotic feature extraction is synergistically employed in conjunction with the C-C method to meticulously discern the pivotal factors that shape the dynamics of PV power, complemented by the inclusion for solar radiation data as an additional element. Besides, a potent fusion of gradient boosting decision trees (GBDT) and BiGRU is leveraged to adeptly process time series data. Moreover, teacher forcing is seamlessly integrated into the model to bolster forecasting accuracy and stability. Experimental validations demonstrate the remarkable performance of the proposed method under complex and diverse weather conditions, offering a pioneering technical approach and theoretical framework for PV power forecasting.</p>

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The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月21日

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  • Guomin Xie,
  • Zijian Zhang,
  • Zhongbao Lin,
  • Sen Xie

摘要

In the face of escalating global energy crises and pressing challenges of environmental pollution, the imperative for sustainable energy solutions has never been more pronounced. Photovoltaic (PV) power generation is recognized as a cornerstone in transition towards a clean energy paradigm. This study introduces a groundbreaking short-term PV power forecasting methodology based on teacher forcing (TF) integrated with bi-directional gated recurrent unit (BiGRU). Firstly, the chaotic feature extraction is synergistically employed in conjunction with the C-C method to meticulously discern the pivotal factors that shape the dynamics of PV power, complemented by the inclusion for solar radiation data as an additional element. Besides, a potent fusion of gradient boosting decision trees (GBDT) and BiGRU is leveraged to adeptly process time series data. Moreover, teacher forcing is seamlessly integrated into the model to bolster forecasting accuracy and stability. Experimental validations demonstrate the remarkable performance of the proposed method under complex and diverse weather conditions, offering a pioneering technical approach and theoretical framework for PV power forecasting.