IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v3y2021i3p34-560d608764.html
   My bibliography  Save this article

Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System

Author

Listed:
  • Peter L. Watson

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Marika Koukoula

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Emmanouil Anagnostou

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

Abstract

Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive analytical methods could be used to help power utilities adapt to these types of storms, but there are uncertainties inherent in the predictability of convective storms that pose a challenge to the accurate prediction of storm-related outages. Describing the strength and localized effects of thunderstorms remains a major technical challenge for meteorologists and weather modelers, and any predictive system for storm impacts will be limited by the quality of the data used to create it. We investigate how the quality of thunderstorm simulations affects power outage models by conducting a comparative analysis, using two different numerical weather prediction systems with different levels of data assimilation. We find that limitations in the weather simulations propagate into the outage model in specific and quantifiable ways, which has implications on how convective storms should be represented to these types of data-driven impact models in the future.

Suggested Citation

  • Peter L. Watson & Marika Koukoula & Emmanouil Anagnostou, 2021. "Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System," Forecasting, MDPI, vol. 3(3), pages 1-20, August.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:3:p:34-560:d:608764
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/3/3/34/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/3/3/34/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    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. Mukherjee, Sayanti & Nateghi, Roshanak & Hastak, Makarand, 2018. "A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 283-305.
    5. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    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.
    7. D. Brent McRoberts & Steven M. Quiring & Seth D. Guikema, 2018. "Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2722-2737, December.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Milad Beikbabaei & Ali Mehrizi-Sani, 2023. "Series FACTS Devices for Increasing Resiliency in Severe Weather Conditions," Energies, MDPI, vol. 16(16), pages 1-14, August.
    2. 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).
    3. Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitris N. Trakas & Mathaios Panteli & Nikos D. Hatziargyriou & Pierluigi Mancarella, 2019. "Spatial Risk Analysis of Power Systems Resilience During Extreme Events," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 195-211, January.
    2. 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).
    3. Tara C. Walsh & David W. Wanik & Emmanouil N. Anagnostou & Jonathan E. Mellor, 2020. "Estimated Time to Restoration of Hurricane Sandy in a Future Climate," Sustainability, MDPI, vol. 12(16), pages 1-27, August.
    4. 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.
    5. 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.
    6. 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).
    7. 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).
    8. Gina L. Tonn & Seth D. Guikema & Celso M. Ferreira & Steven M. Quiring, 2016. "Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1936-1947, October.
    9. Shield, Stephen A. & Quiring, Steven M. & Pino, Jordan V. & Buckstaff, Ken, 2021. "Major impacts of weather events on the electrical power delivery system in the United States," Energy, Elsevier, vol. 218(C).
    10. Rachunok, Benjamin & Nateghi, Roshanak, 2020. "The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    11. D. Brent McRoberts & Steven M. Quiring & Seth D. Guikema, 2018. "Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2722-2737, December.
    12. Kelsea Best & Siobhan Kerr & Allison Reilly & Anand Patwardhan & Deb Niemeier & Seth Guikema, 2023. "Spatial regression identifies socioeconomic inequality in multi-stage power outage recovery after Hurricane Isaac," 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. 117(1), pages 851-873, May.
    13. Otto, Räisänen & Susanne, Suvanto & Jouni, Haapaniemi & Jukka, Lassila, 2023. "Crown snow load outage risk model for overhead lines," Applied Energy, Elsevier, vol. 343(C).
    14. Roshanak Nateghi & Seth Guikema & Steven M. Quiring, 2014. "Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models," Risk Analysis, John Wiley & Sons, vol. 34(6), pages 1069-1078, June.
    15. 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.
    16. Xue, Jiayue & Mohammadi, Farshad & Li, Xin & Sahraei-Ardakani, Mostafa & Ou, Ge & Pu, Zhaoxia, 2020. "Impact of transmission tower-line interaction to the bulk power system during hurricane," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    17. Oh, Seongmun & Jufri, Fauzan Hanif & Choi, Min-Hee & Jung, Jaesung, 2022. "A study of tropical cyclone impact on the power distribution grid in South Korea for estimating damage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    18. Gallaher, Adam & Graziano, Marcello & Fiaschetti, Maurizio, 2021. "Legacy and shockwaves: A spatial analysis of strengthening resilience of the power grid in Connecticut," Energy Policy, Elsevier, vol. 159(C).
    19. Hou, Hui & Liu, Chao & Wei, Ruizeng & He, Huan & Wang, Lei & Li, Weibo, 2023. "Outage duration prediction under typhoon disaster with stacking ensemble learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    20. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:3:y:2021:i:3:p:34-560:d:608764. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.