skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Nonparametric dynamic modeling

Authors:
;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1402088
Grant/Contract Number:
HHSN272201200031C
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Mathematical Biosciences
Additional Journal Information:
Journal Volume: 287; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-06 22:02:39; Journal ID: ISSN 0025-5564
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Faraji, Mojdeh, and Voit, Eberhard O. Nonparametric dynamic modeling. United States: N. p., 2017. Web. doi:10.1016/j.mbs.2016.08.004.
Faraji, Mojdeh, & Voit, Eberhard O. Nonparametric dynamic modeling. United States. doi:10.1016/j.mbs.2016.08.004.
Faraji, Mojdeh, and Voit, Eberhard O. Mon . "Nonparametric dynamic modeling". United States. doi:10.1016/j.mbs.2016.08.004.
@article{osti_1402088,
title = {Nonparametric dynamic modeling},
author = {Faraji, Mojdeh and Voit, Eberhard O.},
abstractNote = {},
doi = {10.1016/j.mbs.2016.08.004},
journal = {Mathematical Biosciences},
number = C,
volume = 287,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.mbs.2016.08.004

Citation Metrics:
Cited by: 2works
Citation information provided by
Web of Science

Save / Share:
  • Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less
    Cited by 2
  • The power to detect linkage for likelihood and nonparametric (Haseman-Elston, affected-sib-pair, and affected-pedigree-member) methods is compared for the case of a common, dichotomous trait resulting from the segregation of two loci. Pedigree data for several two-locus epistatic and heterogeneity models have been simulated, with one of the loci linked to a marker locus. Replicate samples of 20 three-generation pedigrees (16 individuals/pedigree) were simulated and then ascertained for having at least 6 affected individuals. The power of linkage detection calculated under the correct two-locus model is only slightly higher than that under a single locus model with reduced penetrance. As expected,more » the nonparametric linkage methods have somewhat lower power than does the lod-score method, the difference depending on the mode of transmission of the linked locus. Thus, for many pedigree linkage studies, the lod-score method will have the best power. However, this conclusion depends on how many times the lod score will be calculated for a given marker. The Haseman-Elston method would likely be preferable to calculating lod scores under a large number of genetic models (i.e., varying both the mode of transmission and the penetrances), since such an analysis requires an increase in the critical value of the lod criterion. The power of the affected-pedigree-member method is lower than the other methods, which can be shown to be largely due to the fact that marker genotypes for unaffected individuals are not used. 31 refs., 1 fig., 5 tabs.« less
  • Because a deterioration in water quality constitutes a direct threat to human health, it is of utmost importance to have flexible nonparametric methods available for detecting and describing trends in water quality time series. A distinct advantage of nonparametric tests is that they are usually very effective when applied to messy environmental data which may, for example, contain many missing observations and not be normally distributed. By applying their enhanced approaches for nonparametric methods to water quality time series, as well as employing well designed simulation experiments, an attempt is made to demonstrate the efficacy of utilizing nonparametric tests inmore » environmental impact assessment.« less
  • The nonparametric density estimation technique (Ahmad, 1982 and 1983; Robinson, 1987) is employed in a statistical analysis aimed at modeling the variations in sunspot-number data compiled by Woelfer for the period 1770-1869. The fundamental principles of the method are introduced, and the results are presented in tables and graphs. The present method is shown to provide a more accurate fit to the data than conventional time-series analyses. 10 refs.