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Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification

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  • Li, Fen
  • Lin, Yilun
  • Guo, Jianping
  • Wang, Yue
  • Mao, Ling
  • Cui, Yang
  • Bai, Yongqing

Abstract

The diffuse radiation is well recognized as a key variable in solar energy assessment, albeit with sorely lacking ground-based measurements. Here, we proposed two novel models to estimate hourly diffuse radiation using the typical meteorological year’s radiation data in Beijing as training samples. Model 1 was a combination of four classical models, including Liu&Jordan, Orgill&Hollands, Erbs and Reindl, in which the weight or coefficient was determined by weather types that were derived from clearness index. In Model 2, the weather type classification was refined by total cloud cover, and the principal component analysis (PCA) was further applied to determine the major meteorological variables for each weather type as model’s input, along with linear fitting. Using sub-typical year’s radiation data as testing samples, the proposed models showed strong extrapolation ability with three statistical metrics: lower mean absolute percentage error and normalized root mean square error but relatively higher correlation coefficient, compared with other models. Finally, these models were verified by the observations in Wuhan. The results indicated that weather type classification and PCA effectively improved model’s performance by eliminating the collinearity between meteorological and environmental variables. Furthermore, both models performed better than any single classical model, irrespective of large-scale weather patterns.

Suggested Citation

  • Li, Fen & Lin, Yilun & Guo, Jianping & Wang, Yue & Mao, Ling & Cui, Yang & Bai, Yongqing, 2020. "Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification," Renewable Energy, Elsevier, vol. 157(C), pages 1222-1232.
  • Handle: RePEc:eee:renene:v:157:y:2020:i:c:p:1222-1232
    DOI: 10.1016/j.renene.2020.05.080
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    References listed on IDEAS

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    1. He, Yongxiu & Pang, Yuexia & Zhang, Qi & Jiao, Zhe & Chen, Qian, 2018. "Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory," Renewable Energy, Elsevier, vol. 122(C), pages 643-653.
    2. Yao, Wanxiang & Zhang, Chunxiao & Hao, Haodong & Wang, Xiao & Li, Xianli, 2018. "A support vector machine approach to estimate global solar radiation with the influence of fog and haze," Renewable Energy, Elsevier, vol. 128(PA), pages 155-162.
    3. Koo, Choongwan & Li, Wenzhuo & Cha, Seung Hyun & Zhang, Shaojie, 2019. "A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques," Renewable Energy, Elsevier, vol. 133(C), pages 575-592.
    4. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
    5. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.
    6. Bart Sweerts & Stefan Pfenninger & Su Yang & Doris Folini & Bob Zwaan & Martin Wild, 2019. "Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data," Nature Energy, Nature, vol. 4(8), pages 657-663, August.
    7. Jacovides, C.P. & Tymvios, F.S. & Assimakopoulos, V.D. & Kaltsounides, N.A., 2006. "Comparative study of various correlations in estimating hourly diffuse fraction of global solar radiation," Renewable Energy, Elsevier, vol. 31(15), pages 2492-2504.
    8. Bakirci, Kadir, 2015. "Models for the estimation of diffuse solar radiation for typical cities in Turkey," Energy, Elsevier, vol. 82(C), pages 827-838.
    9. Demain, Colienne & Journée, Michel & Bertrand, Cédric, 2013. "Evaluation of different models to estimate the global solar radiation on inclined surfaces," Renewable Energy, Elsevier, vol. 50(C), pages 710-721.
    10. Chan, A.L.S., 2016. "Generation of typical meteorological years using genetic algorithm for different energy systems," Renewable Energy, Elsevier, vol. 90(C), pages 1-13.
    11. Kuo, Chia-Wei & Chang, Wen-Chey & Chang, Keh-Chin, 2014. "Modeling the hourly solar diffuse fraction in Taiwan," Renewable Energy, Elsevier, vol. 66(C), pages 56-61.
    12. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
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    Cited by:

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    3. Starke, Allan R. & Lemos, Leonardo F.L. & Barni, Cristian M. & Machado, Rubinei D. & Cardemil, José M. & Boland, John & Colle, Sergio, 2021. "Assessing one-minute diffuse fraction models based on worldwide climate features," Renewable Energy, Elsevier, vol. 177(C), pages 700-714.
    4. Hassan, Muhammed A. & Abubakr, Mohamed & Khalil, Adel, 2021. "A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals," Renewable Energy, Elsevier, vol. 167(C), pages 613-628.
    5. Yazdani, Hamed & Yaghoubi, Mahmood, 2021. "Techno-economic study of photovoltaic systems performance in Shiraz, Iran," Renewable Energy, Elsevier, vol. 172(C), pages 251-262.
    6. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.

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