The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling
Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/3/3/31 |
| _version_ | 1797519314141577216 |
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| author | Feifei Yang Diego Cerrai Emmanouil N. Anagnostou |
| author_facet | Feifei Yang Diego Cerrai Emmanouil N. Anagnostou |
| author_sort | Feifei Yang |
| collection | DOAJ |
| description | Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times. |
| first_indexed | 2024-03-10T07:41:05Z |
| format | Article |
| id | doaj.art-e8f48cf0e7174e60af62fb0fd07de6be |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-10T07:41:05Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-e8f48cf0e7174e60af62fb0fd07de6be2023-11-22T13:06:27ZengMDPI AGForecasting2571-93942021-07-013350151610.3390/forecast3030031The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction ModelingFeifei Yang0Diego Cerrai1Emmanouil N. Anagnostou2Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USADepartment of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USAWeather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.https://www.mdpi.com/2571-9394/3/3/31forecast uncertaintylead timesevere weathermachine learningpower outages |
| spellingShingle | Feifei Yang Diego Cerrai Emmanouil N. Anagnostou The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling Forecasting forecast uncertainty lead time severe weather machine learning power outages |
| title | The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling |
| title_full | The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling |
| title_fullStr | The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling |
| title_full_unstemmed | The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling |
| title_short | The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling |
| title_sort | effect of lead time weather forecast uncertainty on outage prediction modeling |
| topic | forecast uncertainty lead time severe weather machine learning power outages |
| url | https://www.mdpi.com/2571-9394/3/3/31 |
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