@Article{ee.2025.062035,
AUTHOR = {Feng Guo, Chen Yang, Dezhong Xia, Jingxiang Xu},
TITLE = {Short-Term Prediction of Photovoltaic Power Based on Improved CNN-LSTM and Cascading Learning},
JOURNAL = {Energy Engineering},
VOLUME = {122},
YEAR = {2025},
NUMBER = {5},
PAGES = {1975--1999},
URL = {http://www.techscience.com/energy/v122n5/60686},
ISSN = {1546-0118},
ABSTRACT = {Short-term photovoltaic (PV) power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling. To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction, a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed. First, Pearson correlation coefficients and mutual information are used to select representative features, reducing the impact of redundant features on model performance. Then, the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM, thereby obtaining feature representations rich in temporal information. Subsequently, a multi-layer cascade structure is developed, progressively integrating prediction results from base learners such as LightGBM, XGBoost, Random Forest (RF), and Extreme Random Forest (ERF) to enhance model performance. Finally, an XGBoost-based meta-learner is utilized to integrate the outputs of the base learners and generate the final prediction results. The entire cascading process adopts a dynamic expansion strategy, where the decision to add new cascade layers is based on the R<sup>2</sup> performance criterion. Experimental results demonstrate that the proposed model achieves high prediction accuracy and robustness under various weather conditions, showing significant improvements over traditional models and providing an effective solution for short-term PV power forecasting.},
DOI = {10.32604/ee.2025.062035}
}