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Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method

Author

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  • Antonio Bracale

    (Department of Engineering, University of Napoli Parthenope, 80143 Naples, Italy)

  • Guido Carpinelli

    (Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, 80125 Naples, Italy)

  • Pasquale De Falco

    (Department of Engineering, University of Napoli Parthenope, 80143 Naples, Italy)

Abstract

Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1011-:d:214129
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    Cited by:

    1. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    2. Mohamed Lotfi & Mohammad Javadi & Gerardo J. Osório & Cláudio Monteiro & João P. S. Catalão, 2020. "A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation," Energies, MDPI, vol. 13(1), pages 1-19, January.
    3. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    4. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    5. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    6. Tomasz Serafin & Bartosz Uniejewski & Rafał Weron, 2019. "Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 12(13), pages 1-12, July.
    7. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.

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