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Title: A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling

Abstract

Document is Abstract

Authors:
 [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1247929
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: GeoComputation 2015, Dallas, TX, USA, 20150520, 20150523
Country of Publication:
United States
Language:
English
Subject:
Energy Modeling; Dasymetric Modeling; Machine Learning

Citation Formats

Stewart, Robert N. A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling. United States: N. p., 2015. Web.
Stewart, Robert N. A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling. United States.
Stewart, Robert N. Thu . "A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling". United States. doi:.
@article{osti_1247929,
title = {A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling},
author = {Stewart, Robert N},
abstractNote = {Document is Abstract},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
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