Forecasting hurricane-induced power outage durations

Accurate estimates of the duration of power outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of power companies and emergency managers. This paper presents a novel statistical approach for estimating power outage durations that is 87 % more accurate than existing models in the literature. The power outage duration models are developed and carefully validated for outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced outage durations and their degree of influence on predicting outage restoration for the utility company service area used as our case study.

[1]  Glenn De ' ath.  BOOSTED TREES FOR ECOLOGICAL MODELING AND PREDICTION , 2007 .

[2]  S. S. Venkata,et al.  Distribution system reliability assessment: momentary interruptions and storms , 1997 .

[3]  Seth D Guikema,et al.  Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[4]  Haibin Liu,et al.  Statistical Forecasting of Electric Power Restoration Times in Hurricanes and Ice Storms , 2007, IEEE Transactions on Power Systems.

[5]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[6]  Seth D. Guikema,et al.  Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region , 2009, Reliab. Eng. Syst. Saf..

[7]  M. Kendall,et al.  An Introduction to the Theory of Statistics. , 1911 .

[8]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[9]  Julie E Shortridge,et al.  Public health and pipe breaks in water distribution systems: analysis with internet search volume as a proxy. , 2014, Water research.

[10]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[11]  Rachel A. Davidson,et al.  Electric Power Distribution System Performance in Carolina Hurricanes , 2003 .

[12]  Seth D. Guikema,et al.  Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory , 2009, Reliab. Eng. Syst. Saf..

[13]  D. Lubkeman,et al.  Large scale storm outage management , 2004, IEEE Power Engineering Society General Meeting, 2004..

[14]  James H. Lambert,et al.  Risk-Informed Investment for Tropical Cyclone Preparedness of Highway Signs, Signals, and Lights , 2013 .

[15]  Seth D Guikema,et al.  Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes , 2011, Risk analysis : an official publication of the Society for Risk Analysis.

[16]  Seth D Guikema,et al.  Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[17]  Roshanak Nateghi.  Modeling hurricane activity in the Atlantic Basin and reliability of power distribution systems impacted by hurricanes in the U.S. , 2012 .

[18]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[19]  Philip Sporn.  Electric-Power Distribution Systems in Wartime , 1942, Transactions of the American Institute of Electrical Engineers.

[20]  James H. Lambert,et al.  Diversification of infrastructure projects for emergent and unknown non‐systematic risks , 2011 .

[21]  Seth D. Guikema,et al.  Importance of soil and elevation characteristics for modeling hurricane-induced power outages , 2011 .

[22]  I. Thomas.  Spatial data aggregation: exploratory analysis of road accidents. , 1996, Accident; analysis and prevention.

[23]  J. Wolfowitz,et al.  Introduction to the Theory of Statistics. , 1951 .

[24]  D. Reed.  Electric utility distribution analysis for extreme winds , 2008 .

[25]  Danling Cheng,et al.  Storm modeling for prediction of power distribution system outages , 2007 .

[26]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .