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Title: A regime-dependent artificial neural network technique for short-range solar irradiance forecasting

Authors:
ORCiD logo; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1359816
Grant/Contract Number:  
[DE-EE0006016]
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Renewable Energy
Additional Journal Information:
Journal Name: Renewable Energy Journal Volume: 89 Journal Issue: C; Journal ID: ISSN 0960-1481
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

McCandless, T. C., Haupt, S. E., and Young, G. S. A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. United Kingdom: N. p., 2016. Web. https://doi.org/10.1016/j.renene.2015.12.030.
McCandless, T. C., Haupt, S. E., & Young, G. S. A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. United Kingdom. https://doi.org/10.1016/j.renene.2015.12.030
McCandless, T. C., Haupt, S. E., and Young, G. S. Fri . "A regime-dependent artificial neural network technique for short-range solar irradiance forecasting". United Kingdom. https://doi.org/10.1016/j.renene.2015.12.030.
@article{osti_1359816,
title = {A regime-dependent artificial neural network technique for short-range solar irradiance forecasting},
author = {McCandless, T. C. and Haupt, S. E. and Young, G. S.},
abstractNote = {},
doi = {10.1016/j.renene.2015.12.030},
journal = {Renewable Energy},
number = C,
volume = 89,
place = {United Kingdom},
year = {2016},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.renene.2015.12.030

Citation Metrics:
Cited by: 14 works
Citation information provided by
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