TY - JOUR
T1 - Regime-dependent short-range solar irradiance forecasting
AU - Mccandless, T. C.
AU - Young, G. S.
AU - Haupt, S. E.
AU - Hinkelman, L. M.
N1 - Funding Information:
aThe National Center for Atmospheric Research is sponsored by the National Science Foundation.
Funding Information:
Acknowledgments. This material is based upon work supported by the U.S. Department of Energy under Sunshot Award DE-EE0006016 and by the National Center for Atmospheric Research, which is sponsored by the National Science Foundation. Funding was also provided to LMH by NREL Subcontract AGG-2-22256-01. We gratefully acknowledge all of the collaborators on the SunCast project for insightful discussions and ideas, including Seth Linden, Sheldon Drobot, Jared Lee, Julia Pearson, David John Gagne, and Tara Jensen. This project would not have been possible without the data from the Sacramento Municipal Utility District and Brookhaven National Laboratory and the help from Thomas Brummet at NCAR for the data quality control and processing. Thanks are given to Matt Rogers and Steve Miller for GOES-East data acquisition, discussion, and quality control and for intellectual conversations that led to innovative applications of satellite data in this study.
Publisher Copyright:
© 2016 American Meteorological Society.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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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U2 - 10.1175/JAMC-D-15-0354.1
DO - 10.1175/JAMC-D-15-0354.1
M3 - Article
AN - SCOPUS:85009446133
VL - 55
SP - 1599
EP - 1613
JO - Journal of Applied Meteorology and Climatology
JF - Journal of Applied Meteorology and Climatology
SN - 1558-8424
IS - 7
ER -