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.
The interaction of severe weather, overhead lines and surrounding trees is the leading cause of outages to electric distribution networks in forested areas. In this paper, we show how utility-specific infrastructure and land cover data, aggregated around overhead lines, can improve outage predictions for Eversource Energy (formerly Connecticut Light and Power), the largest electric utility in Connecticut. Eighty-nine storms from different seasons (cold weather, warm weather, transition months) in the period 2005-2014, representing varying types (thunderstorms, blizzards, nor'easters, hurricanes) and outage severity, were simulated using the Weather Research and Forecasting (WRF) atmospheric model. WRF simulations were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This framework could be used for predicting outages to other types of critical infrastructure networks with benefits for emergency-preparedness functions in terms of equipment staging and resource allocation.
This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.
Hurricane Sandy (2012, referred to as Current Sandy) was among the most devastating storms to impact Connecticut’s overhead electric distribution network, resulting in over 15 000 outage locations that affected more than 500 000 customers. In this paper, the severity of tree-caused outages in Connecticut is estimated under future-climate Hurricane Sandy simulations, each exhibiting strengthened winds and heavier rain accumulation over the study area from large-scale thermodynamic changes in the atmosphere and track changes in the year ~2100 (referred to as Future Sandy). Three machine-learning models used five weather simulations and the ensemble mean of Current and Future Sandy, along with land-use and overhead utility infrastructure data, to predict the severity and spatial distribution of outages across the Eversource Energy service territory in Connecticut. To assess the influence of increased precipitation from Future Sandy, two approaches were compared: an outage model fit with a full set of variables accounting for both wind and precipitation, and a reduced set with only wind. Future Sandy displayed an outage increase of 42%–64% when using the ensemble of WRF simulations fit with three different outage prediction models. This study is a proof of concept for the assessment of increased outage risk resulting from potential changes in tropical cyclone intensity associated with late-century thermodynamic changes driven by the IPCC AR4 A2 emissions scenario.
Current state-of-the art regional numerical weather forecasts are run at horizontal grid spacings of a few kilometers, which permits medium to large-scale convective systems to be represented explicitly in the model. With the convection parameterization no longer active, much uncertainty in the formulation of subgrid-scale processes moves to other areas such as the cloud microphysical, turbulence, and land-surface parameterizations. The goal of this study is to investigate experiments with stochastically-perturbed parameters (SPP) within a microphysics parameterization and the model’s horizontal diffusion coefficients. To estimate the “true” uncertainty due to parameter uncertainty, the magnitudes of the perturbations are chosen as realistic as possible and not with purposeful intent of maximal forecast impact as some prior work has done. Spatial inhomogeneities and temporal persistence are represented using a random perturbation pattern with spatial and temporal correlations. The impact on the distributions of various hydrometeors, precipitation characteristics, and solar/longwave radiation are quantified for a winter and summer case. In terms of upscale error growth, the impact is relatively small and consists primarily of triggering atmospheric instabilities in convectively unstable regions. In addition, small in situ changes with potentially large socio-economic impacts are observed in the precipitation characteristics such as maximum hail size. Albeit the impact of introducing physically-based parameter uncertainties within the bounds of aerosol uncertainties is small, their influence on the solar and longwave radiation balances may still have important implications for global model simulations of future climate scenarios.
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