Regime-dependent short-range solar irradiance forecasting

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.

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Author McCandless, Tyler
Young, G.
Haupt, Sue Ellen
Hinkelman, L.
Publisher UCAR/NCAR - Library
Publication Date 2016-07-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T20:47:24.691454
Metadata Record Identifier edu.ucar.opensky::articles:18676
Metadata Language eng; USA
Suggested Citation McCandless, Tyler, Young, G., Haupt, Sue Ellen, Hinkelman, L.. (2016). Regime-dependent short-range solar irradiance forecasting. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7f47qs6. Accessed 16 August 2025.

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