Sensitivity Analysis of Machine Learning Algorithms for Outage Risk Prediction
Files
Date
2024-01-03
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
3150
Ending Page
Alternative Title
Abstract
Severe weather conditions are known for causing forced outages in the electric distribution grid. Recent research efforts were aimed at predicting outages using weather and historical outage data. This paper studies the sensitivity of different Machine Learning (ML) algorithms to the inclusion of weather parameters from adjacent geographic areas and data availability. We analyzed the ability of different ML algorithms to predict electric grid outage State of Risk (SoR). The selected algorithms are trained and tested on actual utility company data. The findings indicate that a bigger size of the training dataset improves the performance of all models, which is measured by the Receiver Operating Curve, Average Precision, and F1 Score. Conducted experiments suggest that at least two years of training data is required to achieve satisfactory performance. Also, we investigate a statistical significance in models’ performance with the inclusion of weather in adjacent geographic areas.
Description
Keywords
Resilient Networks, ml, outage prediction, state of risk
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.