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Short-term probabilistic forecasts for Direct Normal Irradiance

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  • Chu, Yinghao
  • Coimbra, Carlos F.M.

Abstract

A k-nearest neighbor (kNN) ensemble model has been developed to generate Probability Density Function (PDF) forecasts for intra-hour Direct Normal Irradiance (DNI). This probabilistic forecasting model, which uses diffuse irradiance measurements and cloud cover information as exogenous feature inputs, adaptively provides arbitrary PDF forecasts for different weather conditions. The proposed models have been quantitatively evaluated using data from different locations characterized by different climates (continental, coastal, and island). The performance of the forecasts is quantified using metrics such as Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Averaged Width (PINAW), Brier Skill Score (BSS), and the Continuous Ranked Probability Score (CRPS), and other standard error metrics. A persistence ensemble probabilistic forecasting model and a Gaussian probabilistic forecasting model are employed to benchmark the performance of the proposed kNN ensemble model. The results show that the proposed model significantly outperform both reference models in terms of all evaluation metrics for all locations when the forecast horizon is greater than 5-min. In addition, the proposed model shows superior performance in predicting DNI ramps.

Suggested Citation

  • Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:526-536
    DOI: 10.1016/j.renene.2016.09.012
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    References listed on IDEAS

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    1. Chu, Yinghao & Li, Mengying & Coimbra, Carlos F.M., 2016. "Sun-tracking imaging system for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 96(PA), pages 792-799.
    2. Yap, Wai Kean & Karri, Vishy, 2015. "An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques," Renewable Energy, Elsevier, vol. 78(C), pages 42-50.
    3. E. B. Iversen & J. M. Morales & J. K. Møller & H. Madsen, 2014. "Probabilistic forecasts of solar irradiance using stochastic differential equations," Environmetrics, John Wiley & Sons, Ltd., vol. 25(3), pages 152-164, May.
    4. Lave, Matthew & Kleissl, Jan, 2010. "Solar variability of four sites across the state of Colorado," Renewable Energy, Elsevier, vol. 35(12), pages 2867-2873.
    5. Li, Mengying & Chu, Yinghao & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term DNI forecasts," Renewable Energy, Elsevier, vol. 86(C), pages 1362-1371.
    6. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    7. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    8. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Real-time prediction intervals for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 83(C), pages 234-244.
    9. Antonio Bracale & Pierluigi Caramia & Guido Carpinelli & Anna Rita Di Fazio & Gabriella Ferruzzi, 2013. "A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control," Energies, MDPI, vol. 6(2), pages 1-15, February.
    10. Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances," Renewable Energy, Elsevier, vol. 80(C), pages 770-782.
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