Dynamic Modeling of Power Outages Caused by Thunderstorms

Berk A. Alpay, David Wanik, Peter Watson, Diego Cerrai, Guannan Liang and Emmanouil Anagnostou
Additional contact information
Berk A. Alpay: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
David Wanik: Department of Operations and Information Management, University of Connecticut, Storrs, CT 06269, USA
Peter Watson: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Diego Cerrai: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Guannan Liang: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
Emmanouil Anagnostou: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA

Forecasting, 2020, vol. 2, issue 2, 1-12

Abstract: Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage.

Keywords: outage prediction; power grid; thunderstorms; recurrent neural network (RNN) (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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