影响因子：9.1
DOI码：10.1016/j.renene.2024.121174
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Renewable Energy
刊物所在地：英国
关键字：Zebra optimization (ZOA); Variational mode decomposition (VMD); Bi-directional long short term memory (BiLSTM); Uncertainty analysis; Coupling prediction
摘要：Accurate prediction of photovoltaic power generation is essential to promoting the active consumption and low-carbon protection. The complex uncertainty of the photovoltaic system itself leads to the deviation in the photovoltaic power prediction. Therefore, we propose a new prediction model for coupled intelligence optimization. First, the photovoltaic power is decomposed into effective mode components using VMD optimized by GWO. Statistical techniques were used to analyze multidimensional uncertainty and extract features, then, optimize the performance of the coupled model. Second, the Zebra optimization (ZOA) establishes an appropriate balance between exploration and utilization to achieve the optimization of the model parameters. In addition, the CNN is used to extract complex features and enhance the correlation between input values and output values. Finally, the power was predicted using the BiLSTM. The results show that applying the statistical technique to the coupled prediction model not only reveals the uncertainty of photovoltaic systems but reduces the prediction error. Among them, the increased by 0.42 %, the values of MAPE, MSE, RMSE, and MAE were reduced to different degrees. It can better optimize the allocation and reasonable consumption of renewable energy, which provides the decision basis for the adjustment of renewable energy structure.
合写作者：封怡雯,彭丽玲,Hsin-Pou Huang
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：121174
学科门类：工学
文献类型：J
卷号：234
ISSN号：0960-1481
是否译文：否
发表时间：2024-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0960148124012424?via%3Dihub