影响因子：1.9
DOI码：10.1063/5.0275575
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Journal of Renewable and Sustainable Energy
刊物所在地：美国
关键字：Photovoltaics; Electric power; Atmospheric radiation; Algorithms and data structure; Artificial intelligence; Artificial neural networks; Support vector machine
摘要：Accurately predicting photovoltaic output is an effective way to address the impact of uncertainty and volatility in photovoltaic power generation. Therefore, this article proposes a new photovoltaic power combination prediction model based on hybrid complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy optimization (PEO) (briefly as CPO), and improved whale optimization algorithm (IWOA) with bidirectional long short-term memory (BiLSTM) (namely, CPO-IWOA-BiLSTM), which explores the fluctuation characteristics, to achieve accurate prediction of short-term photovoltaic power. Using CEEMDAN technology to decompose photovoltaic power into different modes, and then using PEO to group and reconstruct the data of each mode, significantly characterizing the fluctuation process of photovoltaic power. The IWOA utilizes adaptive weights to improve convergence speed, optimizing the parameters of the BiLSTM model, and thereby improving prediction accuracy. Using kernel density estimation (KDE) to accurately predict the fluctuation range of photovoltaic output power and overcome the uncertainty of photovoltaic output power. The experimental results show that the proposed model can effectively achieve short-term photovoltaic power generation prediction and guide power grid scheduling.
合写作者：Qing-Yi Ge,范国峰
第一作者：彭丽玲
论文类型：期刊论文
通讯作者：洪维强
论文编号：053505
学科门类：工学
文献类型：J
卷号：17
期号：5
ISSN号：1941-7012
是否译文：否
发表时间：2025-01-01
收录刊物：SCI
发布期刊链接：https://pubs.aip.org/aip/jrse/article/17/5/053505/3366174/Short-term-photovoltaic-power-prediction-based-on