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Energies 2018, 11(1), 70; doi:10.3390/en11010070

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

Electrical Engineering, Konkuk University, Seoul 05029, Korea
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Received: 30 November 2017 / Revised: 17 December 2017 / Accepted: 25 December 2017 / Published: 29 December 2017
(This article belongs to the Section Energy Sources)
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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. View Full-Text
Keywords: ensemble forecasting; gradient boosting algorithm; total daily solar irradiation; input data classification; kriging ensemble forecasting; gradient boosting algorithm; total daily solar irradiation; input data classification; kriging
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Baek, M.-K.; Lee, D. Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing. Energies 2018, 11, 70.

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