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A novel adaptive approach for hourly solar radiation forecasting

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  • Akarslan, Emre
  • Hocaoglu, Fatih Onur

Abstract

Solar radiation forecasting is an important part of planning and sizing of a photovoltaic power plant. Yearly measured hourly solar radiation data on the surface of a region include both stochastic and deterministic behaviors. The deterministic part comes from the solar geometry whereas the stochastic part is occurred due to random atmospheric events such as the motion of clouds etc. Moving from these facts, in this paper two different adaptive approaches are developed and tested for hourly solar radiation forecasting. In first approach, the data is separated into seasons. For winter and summer season it is thought that linear predictors work better due to rare alterations for short time periods. For these seasons linear prediction approach is adopted and used. On the other hand bigger alterations are most probable for spring and fall seasons. Therefore, for these seasons an empirical method is employed. In second approach, clearness index is considered as a decision maker to decide whether linear or empirical method will be used as a predictor. This decision is adopted for each prediction. It is obtained from the results that such an adoptive method outperforms non adoptive ones.

Suggested Citation

  • Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:628-633
    DOI: 10.1016/j.renene.2015.10.063
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    6. Akarslan, Emre & Hocaoglu, Fatih Onur, 2017. "A novel method based on similarity for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 112(C), pages 337-346.
    7. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    8. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    9. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    10. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    11. Gürtürk, Mert & Ucar, Ferhat & Erdem, Murat, 2022. "A novel approach to investigate the effects of global warming and exchange rate on the solar power plants," Energy, Elsevier, vol. 239(PD).
    12. Xu, Xiaojun & Du, Huaqiang & Zhou, Guomo & Mao, Fangjie & Li, Pingheng & Fan, Weiliang & Zhu, Dien, 2016. "A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products," Energy, Elsevier, vol. 111(C), pages 117-125.
    13. Sourav Malakar & Saptarsi Goswami & Bhaswati Ganguli & Amlan Chakrabarti & Sugata Sen Roy & K. Boopathi & A. G. Rangaraj, 2022. "Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering," Energies, MDPI, vol. 15(10), pages 1-16, May.
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