A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling
Abstract
As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for high resolution spatially explicit estimates for energy and water demand has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy and water consumption, many are provided at a course spatial resolution or rely on techniques which depend on detailed region-specific data sources that are not publicly available for many parts of the U.S. Furthermore, many existing methods do not account for errors in input data sources and may therefore not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more flexible Monte-Carlo simulation approach to high-resolution residential and commercial electricity and water consumption modeling that relies primarily on publicly available data sources. The method’s flexible data requirement and statistical framework ensure that the model is both applicable to a wide range of regions and reflective of uncertainties in model results.
- Authors:
-
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1311230
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: American Association of Geographers (AAG) - San Francisco, Washington, United States of America - 3/29/2016 12:00:00 AM-4/2/2016 12:00:00 AM
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Energy Modeling; Water Modeling; Monte-Carlo Simulation; Uncertainty Quantification
Citation Formats
Morton, April, Mcmanamay, Ryan, Nagle, Nicholas, Piburn, Jesse, Stewart, Robert, and Surendrannair, Sujithkumar. A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling. United States: N. p., 2016.
Web.
Morton, April, Mcmanamay, Ryan, Nagle, Nicholas, Piburn, Jesse, Stewart, Robert, & Surendrannair, Sujithkumar. A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling. United States.
Morton, April, Mcmanamay, Ryan, Nagle, Nicholas, Piburn, Jesse, Stewart, Robert, and Surendrannair, Sujithkumar. 2016.
"A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling". United States. https://www.osti.gov/servlets/purl/1311230.
@article{osti_1311230,
title = {A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling},
author = {Morton, April and Mcmanamay, Ryan and Nagle, Nicholas and Piburn, Jesse and Stewart, Robert and Surendrannair, Sujithkumar},
abstractNote = {As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for high resolution spatially explicit estimates for energy and water demand has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy and water consumption, many are provided at a course spatial resolution or rely on techniques which depend on detailed region-specific data sources that are not publicly available for many parts of the U.S. Furthermore, many existing methods do not account for errors in input data sources and may therefore not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more flexible Monte-Carlo simulation approach to high-resolution residential and commercial electricity and water consumption modeling that relies primarily on publicly available data sources. The method’s flexible data requirement and statistical framework ensure that the model is both applicable to a wide range of regions and reflective of uncertainties in model results.},
doi = {},
url = {https://www.osti.gov/biblio/1311230},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 01 00:00:00 EDT 2016},
month = {Fri Apr 01 00:00:00 EDT 2016}
}