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

Sustainability

卷:12

Diego Cerrai¹  EmmanouilN. Anagnostou¹  Feifei Yang¹  MdAbul Ehsan Bhuiyan²  DavidW. Wanik³ 

[1] Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA;

[2] Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA;

[3] Department of Operations and Information Management, University of Connecticut, Stamford, CT 06901, USA;

关键词: cross entropy;    event severity representativeness;    machine learning;    outage prediction model;    sample size;    severe weather;    uncertainty;   

DOI  :  10.3390/su12041525

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown