Xie Guomin, Zhang Zijian, Lin Zhongbao, Xie Sen
Institute of Electrical and Control Engineering, Liaoning Technical University, Huludao, 125100, China.
Institute of Intelligence Science and Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China.
Sci Rep. 2025 Apr 21;15(1):13790. doi: 10.1038/s41598-025-99109-2.
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.
面对不断升级的全球能源危机和环境污染的紧迫挑战，可持续能源解决方案的必要性从未如此凸显。光伏发电被视为向清洁能源模式转型的基石。本研究介绍了一种基于教师强制（TF）与双向门控循环单元（BiGRU）相结合的开创性短期光伏发电预测方法。首先，协同运用混沌特征提取和C-C方法，精心识别影响光伏发电动态的关键因素，并纳入太阳辐射数据作为补充要素。此外，利用梯度提升决策树（GBDT）和BiGRU的有效融合来灵活处理时间序列数据。此外，教师强制被无缝集成到模型中，以提高预测的准确性和稳定性。实验验证表明，该方法在复杂多样的天气条件下具有卓越性能，为光伏发电预测提供了开创性的技术方法和理论框架。
