A Quasi-Binomial Regression Model for Hurricane-Induced Power Outages during Early Warning
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 2
Abstract
Hurricanes can cause devastating damage to overhead distribution lines leading to large power outages in electric grids. Power outage prediction models can help utilities to plan for an expedited power recovery by identifying the extent of power disruptions before the arrival of a hurricane. These models often use multiple input parameters, including early warning forecasts of hurricane characteristics, environmental data, power system details, and demographic information. We propose a quasi-binomial regression model to advance power outage models and overcome their existing limitations, such as unbounded outage predictions, limited extrapolation, and high uncertainties at low and high winds. This paper shows that the quasi-binomial model allows us to better capture the mechanics of power system failures due to hurricanes. We fitted our model to power outage data across 2,322 cities for four historical hurricanes: Harvey (2017), Michael (2018), Isaias (2020), and Ida (2021). We validated our model for the outages in Florida during Hurricane Ian (2022). The quasi-binomial model outperformed existing random forest and negative binomial regression models with a 7% error versus 50% and 76%, respectively. To demonstrate the quasi-binomial model’s good performance more comprehensively, we also tested a new beta regression model for outages. We show the quasi-binomial model had a smaller cross-validation root-mean squared error of 0.23 compared with 0.28 for the beta model. Finally, we show that our model also captures that grids with more redundant components can be more resilient to hurricane-caused outages. Thus, our proposed quasi-binomial model advances the state of the art for power outage predictions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All codes supporting this study’s findings are available from the corresponding author upon reasonable request. Power outage data is obtained from PowerOutage (PowerOutage 2022). The source for all other data is provided in the article, and data are available from the authors upon reasonable request.
Acknowledgments
The authors are thankful for the financial support provided by the NYU Tandon School of Engineering Fellowship and NYU CUSP Dissertation Fellowship. This research is also partially supported by the Coalition for Disaster Resilient Infrastructure Fellowship (Grant No. 201924669).
References
AJOT (American Journal of Transportation). 2021. “Hurricane Ida caused at least 1.2 million electricity customers to lose power | AJOT.COM.” Accessed September 1, 2022. https://ajot.com/news/hurricane-ida-caused-at-least-1.2-million-electricity-customers-to-lose-power>(9).
American Community Survey. 2019. “American community survey.” Accessed September 1, 2022. https://www.census.gov/programs-surveys/acs.
Ankit, A., Z. Liu, S. B. Miles, and Y. Choe. 2021. “U.S. resilience to large-scale power outages in 2002–2019.” J. Saf. Sci. Resilience 3 (2): 128–135. https://doi.org/10.1016/j.jnlssr.2022.02.002.
Arora, P., and L. Ceferino. 2023a. “Could rooftop solar panels and storage have enhanced the electricity resilience during Hurricane Isaias (2020)?” In Proc., ICASP 14. Dublin, Ireland: Trinity College Dublin.
Arora, P., and L. Ceferino. 2023b. “Probabilistic and machine learning methods for uncertainty quantification in power outage prediction due to extreme events.” Nat. Hazards Earth Syst. Sci. 23 (5): 1665–1683. https://doi.org/10.5194/nhess-23-1665-2023.
Birchfield, A. B., K. M. Gegner, T. Xu, K. S. Shetye, and T. J. Overbye. 2017. “Statistical considerations in the creation of realistic synthetic power grids for geomagnetic disturbance studies.” IEEE Trans. Power Syst. 32 (2): 1502–1510. https://doi.org/10.1109/TPWRS.2016.2586460.
Bjarnadottir, S., Y. Li, and M. G. Stewart. 2013. “Hurricane risk assessment of power distribution poles considering impacts of a changing climate.” J. Infrastruct. Syst. 19 (1): 12–24. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000108.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (Mar): 5–32. https://doi.org/10.1023/A:1010933404324.
Brown, R. E. 2002. “Electric power distribution reliability.” Accessed September 1, 2022. http://www.dekker.com.
Bucci, L., L. Alaka, A. Hagen, S. Delgado, and J. Beven. 2022. National Hurricane Center tropical cyclone report: Hurricane Ian, 1–72. Miami-Dade County, FL: National Hurricane Center.
Campbell, A. F. 2018. “Puerto Rico power restored 11 months after Hurricane Maria–Vox.” Accessed May 1, 2023. https://www.vox.com/identities/2018/8/15/17692414/puerto-rico-power-electricity-restored-hurricane-maria.
Cangialosi, J. P., E. Blake, M. DeMaria, A. Penny, A. Latto, E. Rappaport, and V. Tallapragada. 2020. “Recent progress in tropical cyclone intensity forecasting at the national hurricane center.” Weather Forecasting 35 (5): 1913–1922. https://doi.org/10.1175/WAF-D-20-0059.1.
Ceferino, L., J. Mitrani-Reiser, A. Kiremidjian, G. Deierlein, and C. Bambarén. 2020. “Effective plans for hospital system response to earthquake emergencies.” Nat. Commun. 11 (1): 1–12. https://doi.org/10.1038/s41467-020-18072-w.
Chapman, L. 2000. “Assessing topographic exposure.” Meteorol. Appl. 7 (4): 335–340. https://doi.org/10.1017/S1350482700001729.
Chavas, D. R., N. Lin, and K. Emanuel. 2015. “A model for the complete radial structure of the tropical cyclone wind field. Part I: Comparison with observed structure.” J. Atmos. Sci. 72 (9): 3647–3662. https://doi.org/10.1175/JAS-D-15-0014.1.
Consul, P. C. 1990. “On some properties and applications of quasi-binomial distribution.” Commun. Stat.-Theory Methods 1 (2): 477–504.
Cortes, M., et al. 2022. Steer: Hurricane Ian preliminary virtual reconnaissance report (PVRR). Salem, OR: DesignSafe-CI.
Cruse, G., and A. Kwasinski. 2021. “Statistical evaluation of flooding impact on power system restoration following a Hurricane.” In Proc., 2021 Resilience Week, RWS 2021. New York: IEEE. https://doi.org/10.1109/RWS52686.2021.9611804.
Danielson, J., and D. Gesh. 2011. “Global multi-resolution terrain elevation data 2010 (GMTED2010): US Geological Survey open-file report 2011–1073.” Accessed March 1, 2023. https://www.usgs.gov/publications/global-multi-resolution-terrain-elevation-data-2010-gmted2010.
DOE. 2013. “Economic benefits of increasing electric grid resilience to weather outages.” Accessed November 8, 2023. https://www.energy.gov/articles/economic-benefits-increasing-electric-grid-resilience-weather-outages.
Douma, J. C., and J. T. Weedon. 2019. “Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression.” Methods Ecol. Evol. 10 (9): 1412–1430. https://doi.org/10.1111/2041-210X.13234.
Dunn, P. K., and G. K. Smyth. 1996. “Randomized quantile residuals.” J. Comput. Graphical Stat. 5 (3): 236–244. https://doi.org/10.2307/1390802.
Dunn, P. K., and G. K. Smyth. 2018. “Generalized linear models with examples in R.” Accessed March 1, 2023. https://link.springer.com/book/10.1007/978-1-4419-0118-7.
Entekhabi, D., R. H. Reichle, R. D. Koster, and W. T. Crow. 2009. “Performance metrics for soil moisture retrievals and application requirements.” Accessed March 1, 2023. http://www.esa.int/esaLP/LPsmos.html.
ESRI (Environmental Systems Research Institute). 2019. ArcGIS desktop: Release 10.8. Redlands, CA: Environmental Systems Research Institute.
Executive Office of the US President. 2022. “Federal register: Critical infrastructure security and resilience month, 2022.” Accessed March 1, 2023. https://www.federalregister.gov/documents/2022/11/03/2022-24148/critical-infrastructure-security-and-resilience-month-2022.
Ferrari, S., and F. Cribari-Neto. 2004. “Beta regression for modelling rates and proportions.” J. Appl. Stat. 31 (7): 799–815. https://doi.org/10.1080/0266476042000214501.
Florida Public Service Commission. 2022. “Hurricane season power outage reports.” Accessed May 1, 2023. https://www.psc.state.fl.us/hurricane-report.
GAO (United States Government Accountability Office). 2020. “Critical infrastructure protection: Actions needed to address significant cybersecurity risks facing the electric grid.” Key Gov. Rep. 56 (Aug): 153–239.
Giulia, C. 2022. “Florida power outage map, update as Hurricane Ian leaves 2M without power.” https://www.newsweek.com/florida-power-outage-map-update-hurricane-ian-leaves-2-million-without-power-1747378.
Guikema, S. D., R. Nateghi, S. M. Quiring, A. Staid, A. C. Reilly, and M. Gao. 2014. “Predicting Hurricane power outages to support storm response planning.” IEEE Access 2 (Sep): 1364–1373. https://doi.org/10.1109/ACCESS.2014.2365716.
Guikema, S. D., S. M. Quiring, and S. R. Han. 2010. “Prestorm estimation of Hurricane damage to electric power distribution systems.” Risk Anal. 30 (12): 1744–1752. https://doi.org/10.1111/j.1539-6924.2010.01510.x.
Guttman, N. B. 1998. “Comparing the palmer drought index and the standardized precipitation index.” J. Am. Water Resour. Assoc. 34 (1): 113–121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x.
Han, S. R., S. D. Guikema, and S. M. Quiring. 2009a. “Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models.” Risk Anal. 29 (10): 1443–1453. https://doi.org/10.1111/j.1539-6924.2009.01280.x.
Han, S. R., S. D. Guikema, S. M. Quiring, K. H. Lee, D. Rosowsky, and R. A. Davidson. 2009b. “Estimating the spatial distribution of power outages during Hurricanes in the Gulf coast region.” Reliab. Eng. Syst. Saf. 94 (2): 199–210. https://doi.org/10.1016/j.ress.2008.02.018.
Hastie, T., R. Tibshirani, and J. Friedman. 2002. Springer series in statistics the elements of statistical learning data mining, inference, and prediction. New York: Springer. https://doi.org/10.1007/978-0-387-84858-7.
Heim, R. R., Jr. 2002. “A review of twentieth-century drought indices used in the United States.” Bull. Am. Meteorol. Soc. 83 (8): 1149–1166. https://doi.org/10.1175/1520-0477-83.8.1149.
Hughes, W., W. Zhang, D. Cerrai, A. Bagtzoglou, D. Wanik, and E. Anagnostou. 2022. “A hybrid physics-based and data-driven model for power distribution system infrastructure hardening and outage simulation.” Reliab. Eng. Syst. Saf. 225 (May): 108628. https://doi.org/10.1016/j.ress.2022.108628.
IEEE. 2007. National electrical safety code. New York: IEEE.
Jin, S., et al. 2021. “National land cover database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance date product.” J. Remote Sens. 3: 0021. https://doi.org/10.34133/remotesensing.0021.
Kira, K., and L. A. Rendell. 1992. “A practical approach to feature selection.” In Proc., Machine Learning, 249–256. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-1-55860-247-2.50037-1.
Klar, B., and S. G. Meintanis. 2012. “Specification tests for the response distribution in generalized linear models.” Comput. Stat. 27 (2): 251–267. https://doi.org/10.1007/s00180-011-0253-5.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann. 2010. “The international best track archive for climate stewardship (IBTrACS).” Bull. Am. Meteorol. Soc. 91 (3): 363–376. https://doi.org/10.1175/2009BAMS2755.1.
Krist, F. J., Jr., J. R. Ellenwood, M. E. Woods, A. J. Mcmahan, J. P. Cowardin, D. E. Ryerson, F. J. Sapio, M. O. Zweifler, and S. A. Romero. 2014. “2013–2027 National insect and disease forest risk assessment.” Accessed May 1, 2023. https://www.fs.usda.gov/foresthealth/technology/pdfs/2012_RiskMap_Report_web.pdf.
Kubinec, R. 2022. “Ordered beta regression: A parsimonious, well-fitting model for continuous data with lower and upper bounds.” In Political analysis, 1–18. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/pan.2022.20.
Kumar, V., and S. Minz. 2014. “Smart computing review feature selection: A literature review.” Smart Comput. Rev. 4 (3): 211–229. https://doi.org/10.6029/smartcr.2014.03.007.
Lin, N., K. Emanuel, M. Oppenheimer, and E. Vanmarcke. 2012. “Physically based assessment of hurricane surge threat under climate change.” Nat. Clim. Change 2 (6): 462–467. https://doi.org/10.1038/nclimate1389.
Liu, F., and Y. Kong. 2015. “ZOIB: An R package for bayesian inference for beta regression and zero/one inflated beta regression.” R J. 7 (2): 34–51. https://doi.org/10.32614/RJ-2015-019.
Liu, H., R. A. Davidson, and T. V. Apanasovich. 2007. “Statistical forecasting of electric power restoration times in hurricanes and ice storms.” IEEE Trans. Power Syst. 22 (4): 2270–2279. https://doi.org/10.1109/TPWRS.2007.907587.
Liu, H., R. A. Davidson, D. V. Rosowsky, and J. R. Stedinger. 2005. “Negative binomial regression of electric power outages in Hurricanes.” J. Infrastruct. Syst. 11 (4): 258–267. https://doi.org/10.1061/(ASCE)1076-0342(2005)11:4(258).
Maderia, C. M. 2015. “Importance of tree species and precipitation for modeling hurricane-induced power outages.” Master’s thesis, Dept. of Geography, Texas A&M Univ.
Markhvida, M., L. Ceferino, and J. W. Baker. 2018. “Modeling spatially correlated spectral accelerations at multiple periods using principal component analysis and geostatistics.” Earthquake Eng. Struct. Dyn. 47 (5): 1107–1123. https://doi.org/10.1002/eqe.3007.
McRoberts, D. B., S. M. Quiring, and S. D. Guikema. 2018. “Improving Hurricane power outage prediction models through the inclusion of local environmental factors.” Risk Anal. 38 (12): 2722–2737. https://doi.org/10.1111/risa.12728.
Miller, C., M. Gibbons, K. Beatty, and A. Boissonnade. 2013. “Topographic speed-up effects and observed roof damage on Bermuda following Hurricane Fabian (2003).” Weather Forecasting 28 (1): 159–174. https://doi.org/10.1175/WAF-D-12-00050.1.
Nateghi, R., S. Guikema, and S. M. Quiring. 2014. “Power outage estimation for tropical cyclones: Improved accuracy with simpler models.” Risk Anal. 34 (6): 1069–1078. https://doi.org/10.1111/risa.12131.
National Academies of Sciences, Engineering, and Medicine. 2017. Enhancing the resilience of the nation's electricity system. Washington, DC: National Academic Press.
Olkin, I., and R. Liu. 2003. “A bivariate beta distribution.” Stat. Probab. Lett. 62 (4): 407–412. https://doi.org/10.1016/S0167-7152(03)00048-8.
Ospina, R., and S. L. Ferrari. 2012. “A general class of zero-or-one inflated beta regression models.” Comput. Stat. Data Anal. 56 (6): 1609–1623. https://doi.org/10.1016/j.csda.2011.10.005.
Pachev, B., P. Arora, C. del Castillo-Negrete, E. Valseth, and C. Dawson. 2023. “A framework for flexible peak storm surge prediction.” Coastal Eng. 186: 104406. https://doi.org/10.1016/j.coastaleng.2023.104406.
Pallone, G. 2020. “FPL crews install underground power lines for pilot program.” Accessed May 1, 2023. https://www.mynews13.com/fl/orlando/news/2020/10/27/florida-power-and-light-underground-power-lines-pilot-program.
Pereira, G. H. 2019. “On quantile residuals in beta regression.” Commun. Stat. - Simul. Comput. 48 (1): 302–316. https://doi.org/10.1080/03610918.2017.1381740.
Petersen, H. C. 1982. “Electricity consumption in rural vs. urban areas.” West. J. Agric. Econ. 7 (1): 13–18.
Pisano, G., N. Chowdhury, M. Coppo, N. Natale, G. Petretto, G. G. Soma, R. Turri, and F. Pilo. 2019. “Synthetic models of distribution networks based on open data and georeferenced information.” Energies 12 (23): 4500. https://doi.org/10.3390/en12234500.
Poudyal, A., C. Wertz, A. Nguyen, S. U. Mahmud, V. Gunturi, and A. Dubey. 2023. “Spatiotemporal impact analysis of hurricanes and storm surges on power systems.” In Proc., 2023 IEEE Power & Energy Society General Meeting (PESGM), 1–5. New York: IEEE.
PowerOutage. 2022. “POWEROUTAGE.US.” Accessed May 1, 2023. https://www.poweroutage.us.
PowerOutage. 2020. “PowerOutage.us on Twitter: Over 3 million electric customers are now without power in the #USA from #Isaias as it continues to move up the east coast: With 1.3 million customers out in #NewJersey.” Accessed May 1, 2023. https://twitter.com/PowerOutage_us/status/1290744180956901379.
Provost, F. 1999. “Distributed data mining: Scaling up and beyond.” In Advances in distributed data mining, edited by H. Kargupta and P. Chan. New York: New York Univ.
Quiring, S. M., L. Zhu, and S. D. Guikema. 2011. “Importance of soil and elevation characteristics for modeling hurricane-induced power outages.” Nat. Hazards 58 (1): 365–390. https://doi.org/10.1007/s11069-010-9672-9.
Schultz, P., J. Heitzig, and J. Kurths. 2014. “A random growth model for power grids and other spatially embedded infrastructure networks.” Eur. Phys. J. Spec. Top. 223 (12): 2593–2610. https://doi.org/10.1140/epjst/e2014-02279-6.
Schweitzer, E., A. Scaglione, A. Monti, and G. A. Pagani. 2017. “Automated generation algorithm for synthetic medium voltage radial distribution systems.” IEEE J. Emerging Sel. Top. Circuits Syst. 7 (2): 271–284. https://doi.org/10.1109/JETCAS.2017.2682934.
Shashaani, S., S. D. Guikema, C. Zhai, J. V. Pino, and S. M. Quiring. 2018. “Multi-stage prediction for zero-inflated hurricane induced power outages.” IEEE Access 6 (Mar): 62432–62449. https://doi.org/10.1109/ACCESS.2018.2877078.
Smith, A. B. 2020. “US billion-dollar weather and climate disasters, 1980—Present (NCEI Accession 0209268).” In National centers for environmental information. Washington, DC: National Oceanic and Atmospheric Administration.
Smithson, M., and J. Verkuilen. 2006. “A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables.” Psychol. Methods 11 (1): 54–71. https://doi.org/10.1037/1082-989X.11.1.54.
Soil Survey Staff. 2021. “Gridded soil survey geographic database (gSSURGO) | Ag data commons.” Accessed January 8, 2021. https://data.nal.usda.gov/dataset/gridded-soil-survey-geographic-database-gssurgo.
Swearingen, C. J., M. S. Melguizo Castro, and Z. Bursac. 2012. “Inflated beta regression: Zero, one, and everything in between.” In SAS global forum. Accessed May 1, 2023. https://support.sas.com/resources/papers/proceedings12/325-2012.pdf.
Tonn, G. L., S. D. Guikema, C. M. Ferreira, and S. M. Quiring. 2016. “Hurricane Isaac: A longitudinal analysis of storm characteristics and power outage risk.” Risk Anal. 36 (10): 1936–1947. https://doi.org/10.1111/risa.12552.
United States Congress. 2020. “H.R.5760-116th Congress (2019–2020): Grid security research and development act | Congress.gov | Library of congress.” Accessed March 1, 2023. https://www.congress.gov/bill/116th-congress/house-bill/5760.
Valenzuela, A., E. Inga, and S. Simani. 2019. “Planning of a resilient underground distribution network using georeferenced data.” Energies 12 (4): 644. https://doi.org/10.3390/en12040644.
Vinutha, H., B. Poornima, and B. Sagar. 2018. “Detection of outliers using interquartile range technique from intrusion dataset.” In Proc., Information and Decision Sciences: Proc., 6th Int. Conf. on FICTA, 511–518. New York: Springer.
Wanik, D. W., J. R. Parent, E. N. Anagnostou, and B. M. Hartman. 2017. “Using vegetation management and LiDAR-derived tree height data to improve outage predictions for electric utilities.” Electr. Power Syst. Res. 146 (Mar): 236–245. https://doi.org/10.1016/j.epsr.2017.01.039.
Winkler, J., L. Dueñas-Osorio, R. Stein, and D. Subramanian. 2010. “Performance assessment of topologically diverse power systems subjected to hurricane events.” Reliab. Eng. Syst. Saf. 95 (4): 323–336. https://doi.org/10.1016/j.ress.2009.11.002.
Wood, S. N. 2017. Generalized additive models: An introduction with R. 2nd ed., 1–476. New York: CRC Press. https://doi.org/10.1201/9781315370279.
Wu, H., M. D. Svoboda, M. J. Hayes, D. A. Wilhite, and F. Wen. 2007. “Appropriate application of the standardized precipitation index in arid locations and dry seasons.” Int. J. Climatol. 27 (1): 65–79. https://doi.org/10.1002/joc.1371.
Xia, Y., et al. 2012a. “Continental-scale water and energy flux analysis and validation for the North American land data assimilation system project phase 2 (NLDAS-2): 1. Intercomparison and application of model products.” J. Geophys. Res.: Atmos. 117 (D3): 3109. https://doi.org/10.1029/2011JD016048.
Xia, Y., et al. 2012b. NLDAS VIC Land surface model L4 hourly 0.125 x 0.125 degree V002. Edited by D. Mocko. Greenbelt, MD: Goddard Earth Sciences Data and Information Services Center.
Yee, T. W. 2012. “Package ‘VGAM’ (Vector generalized linear and additive models).” Accessed May 1, 2023. http://www.springer.com/series/692.
Zhai, C., T. Y. J. Chen, A. G. White, and S. D. Guikema. 2021. “Power outage prediction for natural hazards using synthetic power distribution systems.” Reliab. Eng. Syst. Saf. 208 (Oct): 107348. https://doi.org/10.1016/j.ress.2020.107348.
Zimmerman, R., Q. Zhu, F. De Leon, and Z. Guo. 2017. “Conceptual modeling framework to integrate resilient and interdependent infrastructure in extreme weather.” J. Infrastruct. Syst. 23 (4): 04017034. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000394.
Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 2 • June 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Aug 16, 2023
Accepted: Jan 8, 2024
Published online: Apr 8, 2024
Published in print: Jun 1, 2024
Discussion open until: Sep 8, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.