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

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
900145
Report Number(s):
UCRL-TR-218541
DOE Contract Number:
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY AND ECONOMY; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats

Johannesson, G, McCollom, T, and Barr, C. Spatial Statistical Procedures to Validate Input Data in Energy Models. United States: N. p., 2006. Web. doi:10.2172/900145.
Johannesson, G, McCollom, T, & Barr, C. Spatial Statistical Procedures to Validate Input Data in Energy Models. United States. doi:10.2172/900145.
Johannesson, G, McCollom, T, and Barr, C. Fri . "Spatial Statistical Procedures to Validate Input Data in Energy Models". United States. doi:10.2172/900145. https://www.osti.gov/servlets/purl/900145.
@article{osti_900145,
title = {Spatial Statistical Procedures to Validate Input Data in Energy Models},
author = {Johannesson, G and McCollom, T and Barr, C},
abstractNote = {},
doi = {10.2172/900145},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 27 00:00:00 EST 2006},
month = {Fri Jan 27 00:00:00 EST 2006}
}

Technical Report:

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  • 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 abovementioned 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 energymore » 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.« less
  • 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 modelsmore » 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.« less
  • The objective of Task 7.lD was to (1) establish a collaborative US-USSR effort to improve and validate our methods of forecasting doses and dose commitments from the direct contamination of food sources, and (2) perform experiments and validation studies to improve our ability to predict rapidly and accurately the long-term internal dose from the contamination of agricultural soil. At early times following an accident, the direct contamination of pasture and food stuffs, particularly leafy vegetation and grain, can be of great importance. This situation has been modeled extensively. However, models employed then to predict the deposition, retention and transport ofmore » radionuclides in terrestrial environments employed concepts and data bases that were more than a decade old. The extent to which these models have been tested with independent data sets was limited. The data gathered in the former-USSR (and elsewhere throughout the Northern Hemisphere) offered a unique opportunity to test model predictions of wet and dry deposition, agricultural foodchain bioaccumulation, and short- and long-term retention, redistribution, and resuspension of radionuclides from a variety of natural and artificial surfaces. The current objective of this project is to evaluate and validate pathway-assessment models applicable to exposure and dose reconstruction at DOE facilities through use of international data sets. This project incorporates the activity of Task 7.lD into a multinational effort to evaluate models and data used for the prediction of radionuclide transfer through agricultural and aquatic systems to humans. It also includes participation in two studies, BIOMOVS (BIOspheric MOdel Validation Study) with the Swedish National Institute for Radiation Protection and VAMP (VAlidation of Model Predictions) with the International Atomic Energy Agency, that address testing the performance of models of radionuclide transport through foodchains.« less
  • The purpose of this work is to evaluate and validate pathway assessment models applicable to exposure and dose reconstruction at DOE facilities through the use of international data sets. This report contains summaries of progress made in the months of June, July, August, and September, 1994.