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Title: Machine Learning of Commercial and Residential Load Components in Northwestern United States

Abstract

The impacts of weather attributes on commercial and residential electricity demands and their components in the northwestern United States were examined. Two machine learning methods, regression tree (RT), and random forest (RF), were integrated and compared. Both RT and RF models provide reliable prediction of commercial cooling load. RF models particularly yield higher accuracy with reduced overfitting.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1575926
Report Number(s):
PNNL-SA-142105
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: The ACM e-Energy 2019 Conference
Country of Publication:
United States
Language:
English

Citation Formats

Zhou, Huifen, Hou, Zhangshuan, Etingov, Pavel V., and Liu, Yuan. Machine Learning of Commercial and Residential Load Components in Northwestern United States. United States: N. p., 2019. Web. doi:10.1145/3307772.3330160.
Zhou, Huifen, Hou, Zhangshuan, Etingov, Pavel V., & Liu, Yuan. Machine Learning of Commercial and Residential Load Components in Northwestern United States. United States. doi:10.1145/3307772.3330160.
Zhou, Huifen, Hou, Zhangshuan, Etingov, Pavel V., and Liu, Yuan. Fri . "Machine Learning of Commercial and Residential Load Components in Northwestern United States". United States. doi:10.1145/3307772.3330160.
@article{osti_1575926,
title = {Machine Learning of Commercial and Residential Load Components in Northwestern United States},
author = {Zhou, Huifen and Hou, Zhangshuan and Etingov, Pavel V. and Liu, Yuan},
abstractNote = {The impacts of weather attributes on commercial and residential electricity demands and their components in the northwestern United States were examined. Two machine learning methods, regression tree (RT), and random forest (RF), were integrated and compared. Both RT and RF models provide reliable prediction of commercial cooling load. RF models particularly yield higher accuracy with reduced overfitting.},
doi = {10.1145/3307772.3330160},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
Other availability
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