Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 11, Issue 11, Pages 1936-1940Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2014.2314315
Keywords
Covariance; Gaussian process regression (GPR); physical parameter retrieval; solar irradiation
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Funding
- Spanish Ministry of Economy and Competitiveness (MINECO) [ECO2010-22065-C03-02, TIN2012-38102-C03-01]
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Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used.
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