Predicting Storm Outages Through New Representations of Weather and Vegetation

Jaemo Yang, Diego Cerrai, David Wanik, Md Abul Ehsan Bhuiyan, Xinxuan Zhang, Maria Frediani, Emmanouil Anagnostou

Research output: Contribution to journalArticlepeer-review

69 Scopus Citations

Abstract

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.

Original languageAmerican English
Article number8656482
Pages (from-to)29639-29654
Number of pages16
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

NREL Publication Number

  • NREL/JA-5D00-73928

Keywords

  • extreme events
  • machine learning
  • numerical weather predictions
  • Power distribution
  • power outage prediction

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