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

Min-Kyu Baek and Duehee Lee
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Min-Kyu Baek: Electrical Engineering, Konkuk University, Seoul 05029, Korea
Duehee Lee: Electrical Engineering, Konkuk University, Seoul 05029, Korea

Energies, 2017, vol. 11, issue 1, 1-18

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.

Keywords: ensemble forecasting; gradient boosting algorithm; total daily solar irradiation; input data classification; kriging (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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