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Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations

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  • Zang, Haixiang
  • Jiang, Xin
  • Cheng, LiLin
  • Zhang, Fengchun
  • Wei, Zhinong
  • Sun, Guoqiang

Abstract

Daily global solar radiation (H) is practically significant for human production and life, especially for solar power generation. Due to the high construction and maintenance cost of solar radiation observation equipment, solar radiation measurement cannot be easily obtained at many sites. In addition, the H estimation fitting model cannot be directly trained in the absence of site-specific historical data of H. Therefore, in this study, the H estimation modeling method is proposed specifically for the sites that cannot afford to install solar radiation measurement equipment. The method includes a novel coding method based on information gain, Pearson correlation coefficient, and principal component analysis (PCA), which analyzes the nonlinear and high-dimensional correlation between meteorological factors and solar radiation to decide the correlation of adjacent sites. Based on the results of the coding method, a hybrid H estimation model combining empirical and machine learning is proposed, which takes the empirical model as the base model, and the machine learning model adaptively assigns the corresponding weights to estimate H. The case studies show the proposed hybrid model outperforms the benchmark models and indicate that the H estimation modeling method can be applied to different regions without solar radiation measurement.

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  • Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:795-808
    DOI: 10.1016/j.renene.2022.06.063
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    1. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    2. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2024. "An interpretable framework for modeling global solar radiation using tree-based ensemble machine learning and Shapley additive explanations methods," Applied Energy, Elsevier, vol. 364(C).

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