Next Article in Journal
Thermal Comfort in the Passenger Compartment Using a 3-D Numerical Analysis and Comparison with Fanger’s Comfort Models
Next Article in Special Issue
A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction
Previous Article in Journal
Effect of RF Power on the Properties of Sputtered-CuS Thin Films for Photovoltaic Applications
Previous Article in Special Issue
Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach
Open AccessArticle

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

National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA
*
Author to whom correspondence should be addressed.
Energies 2020, 13(3), 689; https://doi.org/10.3390/en13030689
Received: 15 November 2019 / Revised: 22 January 2020 / Accepted: 1 February 2020 / Published: 5 February 2020
(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)
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. View Full-Text
Keywords: solar power forecasting; machine learning; artificial intelligence; regression tree; artificial neural networks; unsupervised learning; supervised learning; regime-identification solar power forecasting; machine learning; artificial intelligence; regression tree; artificial neural networks; unsupervised learning; supervised learning; regime-identification
Show Figures

Figure 1

MDPI and ACS Style

McCandless, T.; Dettling, S.; Haupt, S.E. Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction. Energies 2020, 13, 689.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop