Short term photovoltaic power prediction based on transfer learning and considering sequence uncertainty

被引:5
作者
Wang, Jiahui [1 ]
Yan, Gaowei [1 ]
Ren, Mifeng [1 ]
Xu, Xinying [1 ]
Ye, Zefu [2 ]
Zhu, Zhujun [2 ]
机构
[1] Taiyuan Univ Technol, Coll Elect & Power Engn, Taiyuan 030024, Shanxi, Peoples R China
[2] GeMeng Int Energy Co Ltd, Taiyuan 030024, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
SOLAR-RADIATION; MODEL; DECOMPOSITION; GENERATION; MACHINE;
D O I
10.1063/5.0126788
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the increasing proportion of solar grid-connected, the establishment of an accurate photovoltaic (PV) power prediction model is very important for safe operation and efficient dispatching of a power grid. Considering the multi-level periodicity of PV power caused by many factors, such as seasons and weather, a short-term PV power prediction model based on transfer component analysis is designed by introducing the idea of transfer learning. In order to measure the uncertainty of numerical weather prediction (NWP) and power sequence, a novel algorithm considering weather similarity and power trend similarity is proposed. First, the intrinsic trend is measured by extracting permutation entropy, variance, and mean from the historical PV power sequence. Second, weighting of NWP is accomplished based on the Pearson correlation coefficient. PV power data are divided into different clusters by K-medoids clustering. At the same time, the transfer component analysis alleviates the time-varying problem of data distribution caused by multi-level time periodicity and effectively improves the prediction accuracy of the model. Finally, simulation experiments are carried out on the PV power output dataset (PVOD). The results show that the prediction accuracy of the proposed method is better than the traditional methods, and the accuracy and applicability of the proposed method are verified.
引用
收藏
页数:12
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