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Real-time prediction intervals for intra-hour DNI forecasts

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  • Chu, Yinghao
  • Li, Mengying
  • Pedro, Hugo T.C.
  • Coimbra, Carlos F.M.

Abstract

We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:234-244
    DOI: 10.1016/j.renene.2015.04.022
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    Cited by:

    1. Nobre, André M. & Severiano, Carlos A. & Karthik, Shravan & Kubis, Marek & Zhao, Lu & Martins, Fernando R. & Pereira, Enio B. & Rüther, Ricardo & Reindl, Thomas, 2016. "PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore," Renewable Energy, Elsevier, vol. 94(C), pages 496-509.
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    3. 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.
    4. Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    5. Trapero, Juan R., 2016. "Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates," Energy, Elsevier, vol. 114(C), pages 266-274.
    6. Huang, Xiaoqiao & Liu, Jun & Xu, Shaozhen & Li, Chengli & Li, Qiong & Tai, Yonghang, 2023. "A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting," Energy, Elsevier, vol. 272(C).
    7. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
    8. Niu, Yinsen & Song, Jifeng & Zou, Lianglin & Yan, Zixuan & Lin, Xilong, 2024. "Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold," Renewable Energy, Elsevier, vol. 226(C).
    9. Nie, Yuhao & Paletta, Quentin & Scott, Andea & Pomares, Luis Martin & Arbod, Guillaume & Sgouridis, Sgouris & Lasenby, Joan & Brandt, Adam, 2024. "Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning," Applied Energy, Elsevier, vol. 369(C).
    10. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    11. Mondal, Rakesh & Roy, Surajit Kr & Giri, Chandan, 2024. "Solar power forecasting using domain knowledge," Energy, Elsevier, vol. 302(C).
    12. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    13. Chu, Yinghao & Wang, Yiling & Yang, Dazhi & Chen, Shanlin & Li, Mengying, 2024. "A review of distributed solar forecasting with remote sensing and deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    14. Zhu, Tingting & Wei, Haikun & Zhao, Xin & Zhang, Chi & Zhang, Kanjian, 2017. "Clear-sky model for wavelet forecast of direct normal irradiance," Renewable Energy, Elsevier, vol. 104(C), pages 1-8.
    15. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    16. Chen, Shanlin & Li, Mengying, 2022. "Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications," Renewable Energy, Elsevier, vol. 189(C), pages 259-272.
    17. Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
    18. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
    19. Chen, Shanlin & Liang, Zhaojian & Dong, Peixin & Guo, Su & Li, Mengying, 2023. "A transferable turbidity estimation method for estimating clear-sky solar irradiance," Renewable Energy, Elsevier, vol. 206(C), pages 635-644.
    20. 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.
    21. Golestaneh, Faranak & Gooi, Hoay Beng & Pinson, Pierre, 2016. "Generation and evaluation of space–time trajectories of photovoltaic power," Applied Energy, Elsevier, vol. 176(C), pages 80-91.
    22. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    23. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.

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