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Title: Spatial Statistical Procedures to Validate Input Data in Energy Models

Abstract

Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy-related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the above-mentioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory andmore » modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.« less

Authors:
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
900140
Report Number(s):
UCRL-TR-218702
TRN: US200709%%548
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
08 HYDROGEN; 29 ENERGY PLANNING, POLICY AND ECONOMY; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; AIR QUALITY; AVAILABILITY; CLIMATES; ECONOMICS; ENERGY MODELS; ENERGY SOURCES; HYDROGEN; MARKET; SIMULATION; STATISTICS

Citation Formats

Lawrence Livermore National Laboratory. Spatial Statistical Procedures to Validate Input Data in Energy Models. United States: N. p., 2006. Web. doi:10.2172/900140.
Lawrence Livermore National Laboratory. Spatial Statistical Procedures to Validate Input Data in Energy Models. United States. doi:10.2172/900140.
Lawrence Livermore National Laboratory. Fri . "Spatial Statistical Procedures to Validate Input Data in Energy Models". United States. doi:10.2172/900140. https://www.osti.gov/servlets/purl/900140.
@article{osti_900140,
title = {Spatial Statistical Procedures to Validate Input Data in Energy Models},
author = {Lawrence Livermore National Laboratory},
abstractNote = {Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy-related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the above-mentioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory and modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.},
doi = {10.2172/900140},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 27 00:00:00 EST 2006},
month = {Fri Jan 27 00:00:00 EST 2006}
}

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