Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration
| Autor: | Emmanouil N. Anagnostou, Feifei Yang, Abul Ehsan Bhuiyan, Diego Cerrai, D. W. Wanik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
0208 environmental biotechnology Geography Planning and Development lcsh:TJ807-830 outage prediction model 0211 other engineering and technologies lcsh:Renewable energy sources 02 engineering and technology Management Monitoring Policy and Law Machine learning computer.software_genre Representativeness heuristic severe weather cross entropy uncertainty lcsh:Environmental sciences Event (probability theory) lcsh:GE1-350 021110 strategic defence & security studies Severe weather Renewable Energy Sustainability and the Environment business.industry lcsh:Environmental effects of industries and plants Training (meteorology) Storm sample size 020801 environmental engineering Cross entropy lcsh:TD194-195 event severity representativeness machine learning Sample size determination Artificial intelligence business computer Predictive modelling |
| Zdroj: | Sustainability Volume 12 Issue 4 Sustainability, Vol 12, Iss 4, p 1525 (2020) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su12041525 |
| Popis: | 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&rsquo 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&rsquo 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|