Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

Tian Han, Ying Wang, Xiao Wang, Kang Chen, Huaiwu Peng, Zhenxin Gao, Lanxin Cui, Wentong Sun and Qinke Peng ()
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Tian Han: Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China
Ying Wang: Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China
Xiao Wang: Qinghai Photovoltaic Industry Centre Co., Ltd., State Power Investment Corporation, Xining 810000, China
Kang Chen: Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Huaiwu Peng: Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Zhenxin Gao: Northwest Engineering Corporation Limited, PowerChina, Xi’an 710065, China
Lanxin Cui: Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China
Wentong Sun: Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China
Qinke Peng: Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an 710049, China

Sustainability, 2023, vol. 15, issue 17, 1-16

Abstract: As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.

Keywords: DNI prediction; pattern regression; CSI model; time series; gradient boosting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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