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A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting

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  • Huang, Jing
  • Perry, Matthew

Abstract

The aim of this work is to produce probabilistic forecasts of solar power for the Global Energy Forecasting Competition 2014 (GEFCom2014). The task involves predicting the outputs from three solar farms at an hourly resolution using data from the ECMWF numerical weather prediction model.

Suggested Citation

  • Huang, Jing & Perry, Matthew, 2016. "A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1081-1086.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1081-1086
    DOI: 10.1016/j.ijforecast.2015.11.002
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    References listed on IDEAS

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    1. Mangalova, E. & Agafonov, E., 2014. "Wind power forecasting using the k-nearest neighbors algorithm," International Journal of Forecasting, Elsevier, vol. 30(2), pages 402-406.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
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    Cited by:

    1. Hugo Bezerra Menezes Leite & Hamidreza Zareipour, 2023. "Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites," Energies, MDPI, vol. 16(3), pages 1-14, February.
    2. Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna, 2023. "Physics-informed Gaussian process regression for states estimation and forecasting in power grids," International Journal of Forecasting, Elsevier, vol. 39(2), pages 967-980.
    3. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Probabilistic solar power forecasting based on weather scenario generation," Applied Energy, Elsevier, vol. 266(C).
    4. Antonio Bracale & Guido Carpinelli & Pasquale De Falco, 2019. "Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Giovanni Brusco & Alessandro Burgio & Daniele Menniti & Anna Pinnarelli & Nicola Sorrentino & Pasquale Vizza, 2017. "Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy," Energies, MDPI, vol. 10(11), pages 1-17, November.
    6. Yaxin Zhang & Tao Hu, 2022. "Ensemble Interval Prediction for Solar Photovoltaic Power Generation," Energies, MDPI, vol. 15(19), pages 1-30, September.
    7. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
    8. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    9. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
    10. Jiang, He & Tao, Changqi & Dong, Yao & Xiong, Ren, 2021. "Robust low-rank multiple kernel learning with compound regularization," European Journal of Operational Research, Elsevier, vol. 295(2), pages 634-647.
    11. David, Mathieu & Luis, Mazorra Aguiar & Lauret, Philippe, 2018. "Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data," International Journal of Forecasting, Elsevier, vol. 34(3), pages 529-547.
    12. Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.
    13. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    14. González Ordiano, Jorge Ángel & Gröll, Lutz & Mikut, Ralf & Hagenmeyer, Veit, 2020. "Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression," International Journal of Forecasting, Elsevier, vol. 36(2), pages 310-323.
    15. Pedro, Hugo T.C. & Coimbra, Carlos F.M. & David, Mathieu & Lauret, Philippe, 2018. "Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 191-203.
    16. Dewangan, Chaman Lal & Singh, S.N. & Chakrabarti, S., 2020. "Combining forecasts of day-ahead solar power," Energy, Elsevier, vol. 202(C).
    17. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
    18. Jizhong Xue & Zaohui Kang & Chun Sing Lai & Yu Wang & Fangyuan Xu & Haoliang Yuan, 2023. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)," Energies, MDPI, vol. 16(11), pages 1-18, May.
    19. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
    20. 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.
    21. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.

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