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The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling

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  • Feifei Yang

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

  • Diego Cerrai

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

  • Emmanouil N. Anagnostou

    (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
    Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA)

Abstract

Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.

Suggested Citation

  • Feifei Yang & Diego Cerrai & Emmanouil N. Anagnostou, 2021. "The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling," Forecasting, MDPI, vol. 3(3), pages 1-16, July.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:3:p:31-516:d:588557
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    References listed on IDEAS

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    1. Liu, Haibin & Davidson, Rachel A. & Apanasovich, Tatiyana V., 2008. "Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 897-912.
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    3. D. Wanik & E. Anagnostou & B. Hartman & M. Frediani & M. Astitha, 2015. "Storm outage modeling for an electric distribution network in Northeastern USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 1359-1384, November.
    4. Feifei Yang & David W. Wanik & Diego Cerrai & Md Abul Ehsan Bhuiyan & Emmanouil N. Anagnostou, 2020. "Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    5. Roshanak Nateghi & Seth D. Guikema & Steven M. Quiring, 2011. "Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes," Risk Analysis, John Wiley & Sons, vol. 31(12), pages 1897-1906, December.
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    Cited by:

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