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A statistical forecast model of weather-related damage to a major electric utility
Descriptive
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Text
TitleInfo
(ID = T-1)
Title
A statistical forecast model of weather-related damage to a major electric utility
Identifier
ETD_2591
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052994
Language
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eng
Genre
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theses
Subject
(ID = SBJ-1)
Name
(authority = LC-NAF)
NamePart
(type = corporate)
Public Service Electric and Gas Company
Subject
(ID = SBJ-2);
(authority = RUETD)
Topic
Atmospheric Science
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Electric utilities--New Jersey
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Storms--New Jersey
Subject
(ID = SBJ-5);
(authority = ETD-LCSH)
Topic
Electric power systems--Natural disaster effects--New Jersey
Abstract
(type = abstract)
A model has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment (plant) of Public Service Electric and Gas (PSE&G), the largest public utility in New Jersey. Utilizing a perfect prognosis approach, the model consists of equations derived from a backwards eliminated multiple linear regression analysis of observed damage (the predictand) and corresponding surface observations from a variety of sources including local storm reports (the predictors). The analysis gives a different equation for each combination of plant damage element (e.g., poles down, transformers blown), the four PSE&G service territories, and objectively defined storm modes (e.g., Thunderstorm, Heat Wave, None). The predictors used most often were found to be products of maximum wind gust with maximum temperature, daily liquid water equivalent precipitation, and ten day accumulated liquid equivalent precipitation, and were often found to be significant (p-value less than 0.05). The number of severe weather reports provided significant predictors for the Thunderstorm storm mode. The resulting regression equations produced coefficients of determination ranging from 0.032 to 0.697 with the lowest values for the None and Cold storm modes, and the highest values for the Thunderstorm and Mix storm modes. The appropriate model equations were applied to an independent verification dataset and the verification standard deviations were compared to the model derived standard errors which revealed heteroscedasticity (predictand error variance is proportional to the predictand itself) in the model. Both error measurements are calculated assuming independence, and they represent a lower-bound on the error estimation because the training dataset was not transformed into a normal distribution and the use of count data for damaged elements yields a non-independent dataset. Two case studies analyzed to critique model performance yielded insight into model shortcomings where lightning information and wind duration were found to be important missing predictors. The case studies were also used to develop guidelines for applying the model in an operational setting. The development of a damage model for other utility companies in other contexts is discussed.
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electronic resource
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vi, 118 p. : ill., maps
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Note
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M.S.
Note
Includes abstract
Note
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Includes bibliographical references
Note
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by Brian John Cerruti
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Cerruti
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Brian John
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1985-
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author
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Brian Cerruti
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Decker
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Steven G.
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chair
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Steven G. Decker
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Broccoli
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Anthony J.
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internal member
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Advisory Committee
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Anthony J. Broccoli
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Miller
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Mark
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Advisory Committee
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Mark Miller
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Lisa
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Rodenburg
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outside member
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Advisory Committee
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Rodenburg Lisa
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
OriginInfo
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2010
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2010
Place
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xx
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T3RV0NSQ
Genre
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ETD graduate
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Rights
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The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
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Name
FamilyName
Cerruti
GivenName
Brian
Role
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Type
Permission or license
DateTime
2010-04-13 19:16:59
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Brian Cerruti
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Technical
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ETD
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application/x-tar
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2129920
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