影响因子：4.2
DOI码：10.1155/er/3423313
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：International Journal of Energy Research
刊物所在地：英国
关键字：deep learning; feature extraction; parameter optimization; photovoltaic power forecasting
摘要：Accurate photovoltaic (PV) power prediction is of great significance to the stable operation of new power systems. However, PV power generation is influenced by meteorological conditions and geographical location, leading to high uncertainty and fluctuations to precise prediction. To effectively capture the complex patterns of PV power generation, this study proposes an intelligent hybrid prediction model. First, an analysis of intrinsic and extrinsic uncertainties of the PV power generation system is conducted to gain a comprehensive understanding of the physical mechanisms underlying power generation. Second, the adaptive empirical wavelet transform (EWT) is employed to decompose the PV power sequence, extracting rich features. Then, considering the volatility, uncertainty, and complexity of those decomposed components, the TOPSIS and the attention mechanism are applied to allocate the feature fusion and associate weights, respectively. Finally, the local features of the data are extracted using the convolutional neural network (CNN), and the dynamic weights of the attention mechanism are used to focus on the key features. These features are then used as the input of the bidirectional long short-term memory (BiLSTM) network. The whale optimization algorithm (WOA) is introduced to adaptively optimize the hyperparameters, thereby improving the training efficiency and accuracy of the model.
合写作者：李金伟,彭丽玲
第一作者：范国峰
论文类型：期刊论文
通讯作者：洪维强
论文编号：3423313
学科门类：工学
文献类型：J
卷号：2026
期号：1
ISSN号：0363-907X
是否译文：否
发表时间：2026
收录刊物：SCI
发布期刊链接：https://onlinelibrary.wiley.com/doi/10.1155/er/3423313