A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

April Morton; Nicholas Nagle; Jesse Piburn; Robert N. Stewart; Ryan McManamay

doi:10.1007/978-3-319-22786-3_5

A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

April Morton, Nicholas Nagle, Jesse Piburn, Robert N. Stewart, Ryan McManamay

Spatial Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method’s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.

Original language	English
Title of host publication	Advances in Geocomputation - Geocomputation 2015—The 13th International Conference
Editors	Daniel A. Griffith, Yongwan Chun, Denis J. Dean
Publisher	Springer Heidelberg
Pages	47-58
Number of pages	12
ISBN (Print)	9783319227856
DOIs	https://doi.org/10.1007/978-3-319-22786-3_5
State	Published - 2017
Event	13th International Conference on Advances in Geocomputation, Geocomputation 2015 - Dallas, United States Duration: May 20 2015 → May 23 2015

Publication series

Name	Advances in Geographic Information Science
ISSN (Print)	1867-2434
ISSN (Electronic)	1867-2442

Conference

Conference	13th International Conference on Advances in Geocomputation, Geocomputation 2015
Country/Territory	United States
City	Dallas
Period	05/20/15 → 05/23/15

Funding

This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy. Accordingly, the United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Keywords

Dasymetric modeling
Energy modeling
Machine learning

Access to Document

10.1007/978-3-319-22786-3_5

Cite this

Morton, A., Nagle, N., Piburn, J., Stewart, R. N., & McManamay, R. (2017). A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling. In D. A. Griffith, Y. Chun, & D. J. Dean (Eds.), Advances in Geocomputation - Geocomputation 2015—The 13th International Conference (pp. 47-58). (Advances in Geographic Information Science). Springer Heidelberg. https://doi.org/10.1007/978-3-319-22786-3_5

Morton, April ; Nagle, Nicholas ; Piburn, Jesse et al. / A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling. Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. editor / Daniel A. Griffith ; Yongwan Chun ; Denis J. Dean. Springer Heidelberg, 2017. pp. 47-58 (Advances in Geographic Information Science).

@inproceedings{4718647dd4d24ebaa449108256620264,

title = "A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling",

abstract = "As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method{\textquoteright}s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.",

keywords = "Dasymetric modeling, Energy modeling, Machine learning",

author = "April Morton and Nicholas Nagle and Jesse Piburn and Stewart, {Robert N.} and Ryan McManamay",

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language = "English",

isbn = "9783319227856",

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Morton, A, Nagle, N, Piburn, J, Stewart, RN & McManamay, R 2017, A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling. in DA Griffith, Y Chun & DJ Dean (eds), Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. Advances in Geographic Information Science, Springer Heidelberg, pp. 47-58, 13th International Conference on Advances in Geocomputation, Geocomputation 2015, Dallas, United States, 05/20/15. https://doi.org/10.1007/978-3-319-22786-3_5

A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling. / Morton, April; Nagle, Nicholas; Piburn, Jesse et al.
Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. ed. / Daniel A. Griffith; Yongwan Chun; Denis J. Dean. Springer Heidelberg, 2017. p. 47-58 (Advances in Geographic Information Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

AU - Morton, April

AU - Nagle, Nicholas

AU - Piburn, Jesse

AU - Stewart, Robert N.

AU - McManamay, Ryan

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2017.

PY - 2017

Y1 - 2017

N2 - As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method’s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.

AB - As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method’s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.

KW - Dasymetric modeling

KW - Energy modeling

KW - Machine learning

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M3 - Conference contribution

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SN - 9783319227856

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BT - Advances in Geocomputation - Geocomputation 2015—The 13th International Conference

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PB - Springer Heidelberg

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ER -

Morton A, Nagle N, Piburn J, Stewart RN, McManamay R. A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling. In Griffith DA, Chun Y, Dean DJ, editors, Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. Springer Heidelberg. 2017. p. 47-58. (Advances in Geographic Information Science). doi: 10.1007/978-3-319-22786-3_5

A hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

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Keywords

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