Predicting Storm Outages Through New Representations of Weather and Vegetation
This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distrib...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8656482/ |
| _version_ | 1818557111071670272 |
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| author | Diego Cerrai David W. Wanik Md Abul Ehsan Bhuiyan Xinxuan Zhang Jaemo Yang Maria E. B. Frediani Emmanouil N. Anagnostou |
| author_facet | Diego Cerrai David W. Wanik Md Abul Ehsan Bhuiyan Xinxuan Zhang Jaemo Yang Maria E. B. Frediani Emmanouil N. Anagnostou |
| author_sort | Diego Cerrai |
| collection | DOAJ |
| description | This paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results show that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses. |
| first_indexed | 2024-12-13T23:55:43Z |
| format | Article |
| id | doaj.art-1ae0567f09f14719905574667b9cb0da |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:55:43Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1ae0567f09f14719905574667b9cb0da2022-12-21T23:26:34ZengIEEEIEEE Access2169-35362019-01-017296392965410.1109/ACCESS.2019.29025588656482Predicting Storm Outages Through New Representations of Weather and VegetationDiego Cerrai0https://orcid.org/0000-0001-5918-4885David W. Wanik1Md Abul Ehsan Bhuiyan2Xinxuan Zhang3Jaemo Yang4Maria E. B. Frediani5Emmanouil N. Anagnostou6Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USAEversource Energy Center, University of Connecticut, Storrs, CT, USADepartment of Natural Resources and the Environment, University of Connecticut, Storrs, CT, USADepartment of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USANational Renewable Energy Laboratory, Golden, CO, USANational Center for Atmospheric Research, Boulder, CO, USADepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USAThis paper introduces new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results show that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses.https://ieeexplore.ieee.org/document/8656482/Power distributionextreme eventsmachine learningnumerical weather predictionspower outage prediction |
| spellingShingle | Diego Cerrai David W. Wanik Md Abul Ehsan Bhuiyan Xinxuan Zhang Jaemo Yang Maria E. B. Frediani Emmanouil N. Anagnostou Predicting Storm Outages Through New Representations of Weather and Vegetation IEEE Access Power distribution extreme events machine learning numerical weather predictions power outage prediction |
| title | Predicting Storm Outages Through New Representations of Weather and Vegetation |
| title_full | Predicting Storm Outages Through New Representations of Weather and Vegetation |
| title_fullStr | Predicting Storm Outages Through New Representations of Weather and Vegetation |
| title_full_unstemmed | Predicting Storm Outages Through New Representations of Weather and Vegetation |
| title_short | Predicting Storm Outages Through New Representations of Weather and Vegetation |
| title_sort | predicting storm outages through new representations of weather and vegetation |
| topic | Power distribution extreme events machine learning numerical weather predictions power outage prediction |
| url | https://ieeexplore.ieee.org/document/8656482/ |
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