RESEARCH ON PHOTOVOLTAIC OUTPUT COMBINATION PREDICTION MODEL BASED ON SIMILAR DAY SELECTION AND PCA-LSTM

被引：0

作者：

Meng, Yikang ^{[1
]}

Xu, Ye ^{[1
]}

Wang, Xinpeng ^{[1
]}

Wang, Tao ^{[1
]}

Li, Wei ^{[1
]}

机构：

[1] College of Environmental Science and Engineering, North China Electric Power University, Beijing

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 07期

关键词：

dynamic time warping; improved K-means; long-short term memory; prediction model; principal component analysis; PV power station;

D O I：

10.19912/j.0254-0096.tynxb.2023-0498

中图分类号：

学科分类号：

摘要：

In this paper，a PV output portfolio forecasting model is constructed by integrating principal component analysis（PCA），an improved K-means clustering method，dynamic time warping（DTW），and a long-short term memory（LSTM）neural network. Based on the PCA method to extract the principal component factors of meteorological elements，the improved K-means clustering method and DTW algorithm are innovatively used to generate a set of historical day samples with a high degree of internal correlation and similar weather characteristics to the day to be predicted. Then，the LSTM neural network is combined to build a PV power prediction model based on the selection of similar days，which finally achieves the accurate prediction of power generation of a PV plant in Yunnan. The comparison results with other prediction models show that the combined prediction model constructed in this paper has better prediction performance and broad application prospects. © 2024 Science Press. All rights reserved.

引用

页码：453 / 461

页数：8

共 20 条

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