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Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network

Author

Listed:
  • Jichao He
  • David W. Wanik
  • Brian M. Hartman
  • Emmanouil N. Anagnostou
  • Marina Astitha
  • Maria E. B. Frediani

Abstract

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.

Suggested Citation

  • Jichao He & David W. Wanik & Brian M. Hartman & Emmanouil N. Anagnostou & Marina Astitha & Maria E. B. Frediani, 2017. "Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 441-458, March.
    • Handle: RePEc:wly:riskan:v:37:y:2017:i:3:p:441-458
    DOI: 10.1111/risa.12652
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    1. Steven Quiring & Laiyin Zhu & Seth Guikema, 2011. "Importance of soil and elevation characteristics for modeling hurricane-induced power outages," 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. 58(1), pages 365-390, July.
    2. 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.
    3. 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.
    4. 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.
    5. Han, Seung-Ryong & Guikema, Seth D. & Quiring, Steven M. & Lee, Kyung-Ho & Rosowsky, David & Davidson, Rachel A., 2009. "Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 199-210.
    6. Roshanak Nateghi & Seth Guikema & Steven Quiring, 2014. "Forecasting hurricane-induced power outage durations," 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. 74(3), pages 1795-1811, December.
    7. Seth D. Guikema & Steven M. Quiring & Seung‐Ryong Han, 2010. "Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems," Risk Analysis, John Wiley & Sons, vol. 30(12), pages 1744-1752, December.
    8. Guikema, S.D. & Quiring, S.M., 2012. "Hybrid data mining-regression for infrastructure risk assessment based on zero-inflated data," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 178-182.
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    Citations

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    Cited by:

    1. Zhai, Chengwei & Chen, Thomas Ying-jeh & White, Anna Grace & Guikema, Seth David, 2021. "Power outage prediction for natural hazards using synthetic power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. Otto, Räisänen & Susanne, Suvanto & Jouni, Haapaniemi & Jukka, Lassila, 2023. "Crown snow load outage risk model for overhead lines," Applied Energy, Elsevier, vol. 343(C).
    3. Hughes, William & Zhang, Wei & Bagtzoglou, Amvrossios C. & Wanik, David & Pensado, Osvaldo & Yuan, Hao & Zhang, Jintao, 2021. "Damage modeling framework for resilience hardening strategy for overhead power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. 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.
    6. Berk A. Alpay & David Wanik & Peter Watson & Diego Cerrai & Guannan Liang & Emmanouil Anagnostou, 2020. "Dynamic Modeling of Power Outages Caused by Thunderstorms," Forecasting, MDPI, vol. 2(2), pages 1-12, May.

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