Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Tyler McCandless, Susan Dettling and Sue Ellen Haupt
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Tyler McCandless: National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA
Susan Dettling: National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA
Sue Ellen Haupt: National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA

Energies, 2020, vol. 13, issue 3, 1-14

Abstract: This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

Keywords: solar power forecasting; machine learning; artificial intelligence; regression tree; artificial neural networks; unsupervised learning; supervised learning; regime-identification (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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