skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Day-ahead hourly electricity load modeling by functional regression

Authors:
;
Publication Date:
Sponsoring Org.:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
OSTI Identifier:
1341098
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Name: Applied Energy Journal Volume: 170 Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Feng, Yonghan, and Ryan, Sarah M. Day-ahead hourly electricity load modeling by functional regression. United Kingdom: N. p., 2016. Web. doi:10.1016/j.apenergy.2016.02.118.
Feng, Yonghan, & Ryan, Sarah M. Day-ahead hourly electricity load modeling by functional regression. United Kingdom. doi:10.1016/j.apenergy.2016.02.118.
Feng, Yonghan, and Ryan, Sarah M. Sun . "Day-ahead hourly electricity load modeling by functional regression". United Kingdom. doi:10.1016/j.apenergy.2016.02.118.
@article{osti_1341098,
title = {Day-ahead hourly electricity load modeling by functional regression},
author = {Feng, Yonghan and Ryan, Sarah M.},
abstractNote = {},
doi = {10.1016/j.apenergy.2016.02.118},
journal = {Applied Energy},
number = C,
volume = 170,
place = {United Kingdom},
year = {2016},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.apenergy.2016.02.118

Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

Save / Share: