Predicting Storm Outages Through New Representations of Weather and Vegetation

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. INDEX TERMS Power distribution, extreme events, machine learning, numerical weather predictions, power outage prediction.

Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration

“…In these last three studies [6][7][8], underestimation bias was exhibited in high-impact events and overestimation bias in low-impact events. Our model further advanced the models developed by Wanik et al [6,8] and quantified the uncertainty of OPMs.…”

“…He et al [7] continued this work using quantile regression forests (QRF) and Bayesian additive regression tree (BART) models to predict power outages in the Northeastern United States. Cerrai et al [8] used 76 extratropical and 44 convective storms, introduced the methods of storm type classification, and further developed the OPM based on the work of Wanik et al [6] and He et al [7]. In these last three studies [6][7][8], underestimation bias was exhibited in high-impact events and overestimation bias in low-impact events.…”

“…Cerrai et al [8] used 76 extratropical and 44 convective storms, introduced the methods of storm type classification, and further developed the OPM based on the work of Wanik et al [6] and He et al [7]. In these last three studies [6][7][8], underestimation bias was exhibited in high-impact events and overestimation bias in low-impact events. Our model further advanced the models developed by Wanik et al [6,8] and quantified the uncertainty of OPMs.…”

“…For example, the hurricane studies mostly used only 10 storms in their models [2][3][4][5], while Wanik et al [6] and He et al [7] used less than 100 events in their OPM. Although Cerrai et al [8] used 120 storms and selected the most important variables for their model, they did not quantify the uncertainty of OPMs with different sample sizes. In addition to the uncertainty associated with the training sample sizes, OPMs exhibit, as mentioned, overestimation biases of low-impact events and underestimation biases of high-impact events [6][7][8].…”

“…Although Cerrai et al [8] used 120 storms and selected the most important variables for their model, they did not quantify the uncertainty of OPMs with different sample sizes. In addition to the uncertainty associated with the training sample sizes, OPMs exhibit, as mentioned, overestimation biases of low-impact events and underestimation biases of high-impact events [6][7][8]. Given that the standard approach has been to use all the historical events as the training dataset, this may be the cause of prediction bias.…”