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Regime-Dependent Short-Range Solar Irradiance Forecasting

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  • 1 National Center for Atmospheric Research, Boulder, Colorado, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 2 Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 3 National Center for Atmospheric Research, Boulder, Colorado, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 4 University of Washington, Seattle, Washington
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Abstract

This paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions of 15-min-average clearness index (global horizontal irradiance). This regime-dependent artificial neural network (RD-ANN) system classifies cloud regimes with a k-means algorithm on the basis of a combination of surface weather observations, irradiance observations, and GOES-East satellite data. The ANNs are then trained on each cloud regime to predict the clearness index. This RD-ANN system improves over the mean absolute error of the baseline clearness-index persistence predictions by 1.0%, 21.0%, 26.4%, and 27.4% at the 15-, 60-, 120-, and 180-min forecast lead times, respectively. In addition, a version of this method configured to predict the irradiance variability predicts irradiance variability more accurately than does a smart persistence technique.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Tyler McCandless, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301. E-mail: mccandle@ucar.edu

Abstract

This paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions of 15-min-average clearness index (global horizontal irradiance). This regime-dependent artificial neural network (RD-ANN) system classifies cloud regimes with a k-means algorithm on the basis of a combination of surface weather observations, irradiance observations, and GOES-East satellite data. The ANNs are then trained on each cloud regime to predict the clearness index. This RD-ANN system improves over the mean absolute error of the baseline clearness-index persistence predictions by 1.0%, 21.0%, 26.4%, and 27.4% at the 15-, 60-, 120-, and 180-min forecast lead times, respectively. In addition, a version of this method configured to predict the irradiance variability predicts irradiance variability more accurately than does a smart persistence technique.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Tyler McCandless, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301. E-mail: mccandle@ucar.edu
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