Prediction of direct normal irradiance from photovoltaic power plants based on clustering and RIME-LightGBM

Authors

Can ZHOU, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Yuca i ZHOU, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaFollow
Yanxiang TAN, School of Mathematics and Statistics, Changsha University of Science & Technology, Changsha 410114, China
Tian XIAO, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Qiyue XIE, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Zhongli SHEN, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Qiang FU, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Yuanheng QIN, School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

Keywords

concentrating solar power; direct normal irradiance; mini batch K-means clustering; light gradientboosting machine; rime optimization algorithm

Abstract

The stability of the power output of the photovoltaic power plants is affected by the intermittency and uncertainty of direct normal irradiance (DNI). In order to solve this problem, a prediction model of DNI based on the clustering and rime optimization algorithm (RIME) optimizing the light gradient boosting machine (LightGBM) is proposed. Firstly, the strongly correlated meteorological parameters of the DNI are determined by the Pearson correlation coefficient, and the historical meteorological data is classified by the mini batch K-means (MBK) clustering algorithm. Then, RIME is used to optimize the hyperparameters of the LightGBM and to establish the prediction model of DNI for different categories of historical meteorological data. The Euclidean distances between the hourly data of the forecast day and the strongly correlated meteorological parameters of each cluster center are used to select the corresponding prediction model for the prediction of the DNI. Finally, by using the historical meteorological data from 2000 to 2019 of a concentrating solar power (CSP) plant in California, USA, the proposed model is validated. The experimental results show that the proposed prediction model can accurately predict the value and variation trend of DNI.

DOI

10.19781/j.issn.1673-9140.2025.06.024

First Page

241

Last Page

249

Recommended Citation

ZHOU, Can; ZHOU, Yuca i; TAN, Yanxiang; XIAO, Tian; XIE, Qiyue; SHEN, Zhongli; FU, Qiang; and QIN, Yuanheng (2026) "Prediction of direct normal irradiance from photovoltaic power plants based on clustering and RIME-LightGBM," Journal of Electric Power Science and Technology: Vol. 40: Iss. 6, Article 24.
DOI: 10.19781/j.issn.1673-9140.2025.06.024
Available at: https://jepst.researchcommons.org/journal/vol40/iss6/24