Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration

Feifei Yang, David W. Wanik, Diego Cerrai, Md Abul Ehsan Bhuiyan and Emmanouil N. Anagnostou
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Feifei Yang: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
David W. Wanik: Department of Operations and Information Management, University of Connecticut, Stamford, CT 06901, USA
Diego Cerrai: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Md Abul Ehsan Bhuiyan: Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA
Emmanouil N. Anagnostou: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA

Sustainability, 2020, vol. 12, issue 4, 1-19

Abstract: A growing number of electricity utilities use machine learning-based outage prediction models (OPMs) to predict the impact of storms on their networks for sustainable management. The accuracy of OPM predictions is sensitive to sample size and event severity representativeness in the training dataset, the extent of which has not yet been quantified. This study devised a randomized and out-of-sample validation experiment to quantify an OPM’s prediction uncertainty to different training sample sizes and event severity representativeness. The study showed random error decreasing by more than 100% for sample sizes ranging from 10 to 80 extratropical events, and by 32% for sample sizes from 10 to 40 thunderstorms. This study quantified the minimum number of sample size for the OPM attaining an acceptable prediction performance. The results demonstrated that conditioning the training of the OPM to a subset of events representative of the predicted event’s severity reduced the underestimation bias exhibited in high-impact events and the overestimation bias in low-impact ones. We used cross entropy (CE) to quantify the relatedness of weather variable distribution between the training dataset and the forecasted event.

Keywords: cross entropy; event severity representativeness; machine learning; outage prediction model; sample size; severe weather; uncertainty (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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