Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

Olukunle O Owolabi,Deborah A Sunter

doi:10.3390/en15061958

Abstract

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.

Highlights

Power outages demonstrate a failure in the proper functioning of an electrical distribution system [1–4]
For the county level Prophet-BU model, we observed an improved performance in the proposed model relative to other hierarchical and Prophet forecasting models (Table 4), indicating that the proposed Prophet-BU model better captures the temporal patterns of Non-Weather Outage Count Index (NWOCI) at the county level
Unlike previous studies that focus on extreme weather outage (EWO) prediction, we highlighted the importance of capturing non-extreme weather outages (NWO), which occur more frequently and have substantial cumulative effect on the electrical distribution grid

Summary

Introduction

Power outages demonstrate a failure in the proper functioning of an electrical distribution system [1–4]. These outage events can result in substantial financial losses [5–7], such as food spoilage [8,9] or a serious health emergency in a health facility [10], especially when back-up sources of generation fail [11]. Outage forecasting models are a useful tool for uncovering historical and future trends of outage events and can, guide outage preventive and mitigation strategies [14]. Outages unassociated with adverse weather events (Non-extreme weather outages or non-weather related outages, NWO) often affect a smaller proportion of electricity customers at a time, such as those emanating from the ecology domain as a result of an animal disturbance [18–20]

Methods

Results

Discussion

Conclusion