Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO.
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Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission
This paper describes the implementation of a prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with a variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of the logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that the developed model manifests high accuracy and is able to differentiate an outage area from the rest of the network in 1 to 3 hours before the outage. The prediction model is integrated inside a weather testbed for real-time mapping of network outage probabilities based on incoming weather forecast.
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- Award ID(s):
- 1636772
- NSF-PAR ID:
- 10110812
- Date Published:
- Journal Name:
- The Hawaii International Conference on System Sciences – HICSS, Maui, Hawaii, January 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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