A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility

Abstract

A model has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment (plant) of Public Service Electric and Gas (PSE&G), the largest public utility in New Jersey. Utilizing a perfect prognosis approach, the model consists of equations derived from a backwards eliminated multiple linear regression analysis of observed damage (the predictand) and corresponding surface observations from a variety of sources including local storm reports (the predictors). The analysis gives a different equation for each combination of plant damage element (e.g., poles down, transformers blown), the four PSE&G service territories, and objectively defined storm modes (e.g., Thunderstorm, Heat Wave, None). The predictors used most often were found to be products of maximum wind gust with maximum temperature, daily liquid water equivalent precipitation, and ten day accumulated liquid equivalent precipitation, and were often found to be significant (p-value less than 0.05). The number of severe weather reports provided significant predictors for the Thunderstorm storm

Resilience of Energy Infrastructure and Services: Modeling, Data Analytics, and Metrics

“…The above models have been used widely in subsequent works [34]- [37]. Resulting models identify fallen trees as major causes of power failures [20], [38]; and transformers as affected most by severe storms [32], [34].…”