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Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

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  • Min-Kyu Baek

    (Electrical Engineering, Konkuk University, Seoul 05029, Korea)

  • Duehee Lee

    (Electrical Engineering, Konkuk University, Seoul 05029, Korea)

Abstract

Total daily solar irradiation for the next day is forecasted through an ensemble of multiple machine learning algorithms using forecasted weather scenarios from numerical weather prediction (NWP) models. The weather scenarios were predicted at grid points whose longitudes and latitudes are integers, but the total daily solar irradiation was measured at non-integer grid points. Therefore, six interpolation functions are used to interpolate weather scenarios at non-integer grid points, and their performances are compared. Furthermore, when the total daily solar irradiation for the next day is forecasted, many data trimming techniques, such as outlier detection, input data clustering, input data pre-processing, and output data post-processing techniques, are developed and compared. Finally, various combinations of these ensemble techniques, different NWP scenarios, and machine learning algorithms are compared. The best model is to combine multiple forecasting machines through weighted averaging and to use all NWP scenarios.

Suggested Citation

  • Min-Kyu Baek & Duehee Lee, 2017. "Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing," Energies, MDPI, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:70-:d:124804
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    References listed on IDEAS

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    1. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    2. Kraas, Birk & Schroedter-Homscheidt, Marion & Pulvermüller, Benedikt & Madlener, Reinhard, 2011. "Economic Assessment of a Concentrating Solar Power Forecasting System for Participation in the Spanish Electricity Market," FCN Working Papers 12/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    2. Ankit Kumar Srivastava & Devender Singh & Ajay Shekhar Pandey & Tarun Maini, 2019. "A Novel Feature Selection and Short-Term Price Forecasting Based on a Decision Tree (J48) Model," Energies, MDPI, vol. 12(19), pages 1-17, September.

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