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Estimation of Resource Allocation Based on Disturbance Prediction Data with Use of Statistics, Machine Learning and Data Analysis

Domeij, Rebecka LU and Luong, Richard LU (2017) In Master's Theses in Mathematical Sciences FMA820 20171
Mathematics (Faculty of Engineering)
Abstract
The electrical power grid is one of modern society’s most important infrastructures and both power distributors and the Swedish government are investing large amount of resources to ensure continuous delivery of power. By predicting future outages with an automatic prediction system, the distributors could prevent long restoration times and economic loss. In this work, the authors evaluate the possibility of predicting outages based on statistical and machine learning methods and the relative importance of the different factors. The study uses open weather data and data on the power grid gathered from Swedish Meteorological Institute and E.ON, respectively.

The result shows that while maintaining the same false positive rate as... (More)
The electrical power grid is one of modern society’s most important infrastructures and both power distributors and the Swedish government are investing large amount of resources to ensure continuous delivery of power. By predicting future outages with an automatic prediction system, the distributors could prevent long restoration times and economic loss. In this work, the authors evaluate the possibility of predicting outages based on statistical and machine learning methods and the relative importance of the different factors. The study uses open weather data and data on the power grid gathered from Swedish Meteorological Institute and E.ON, respectively.

The result shows that while maintaining the same false positive rate as currently used manual prediction methods, the automatic prediction is able to increase the true positive rate from 20% to 33%. The authors conclude that wind gust is the most important factor in predicting weather related outages and that the models are better to predict outages during stronger winds. However, further data analysis is warranted before the automatic prediction can be implemented in a real world context. (Less)
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author
Domeij, Rebecka LU and Luong, Richard LU
supervisor
organization
course
FMA820 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
outages, prediction, negative binomial regression, logistic regression, decision tree
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3316-2017
ISSN
1404-6342
other publication id
2017:E19
language
English
id
8907090
date added to LUP
2017-05-29 16:22:19
date last changed
2017-05-29 16:22:19
@misc{8907090,
  abstract     = {The electrical power grid is one of modern society’s most important infrastructures and both power distributors and the Swedish government are investing large amount of resources to ensure continuous delivery of power. By predicting future outages with an automatic prediction system, the distributors could prevent long restoration times and economic loss. In this work, the authors evaluate the possibility of predicting outages based on statistical and machine learning methods and the relative importance of the different factors. The study uses open weather data and data on the power grid gathered from Swedish Meteorological Institute and E.ON, respectively. 

The result shows that while maintaining the same false positive rate as currently used manual prediction methods, the automatic prediction is able to increase the true positive rate from 20% to 33%. The authors conclude that wind gust is the most important factor in predicting weather related outages and that the models are better to predict outages during stronger winds. However, further data analysis is warranted before the automatic prediction can be implemented in a real world context.},
  author       = {Domeij, Rebecka and Luong, Richard},
  issn         = {1404-6342},
  keyword      = {outages,prediction,negative binomial regression,logistic regression,decision tree},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Estimation of Resource Allocation Based on Disturbance Prediction Data with Use of Statistics, Machine Learning and Data Analysis},
  year         = {2017},
}