Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages
Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages
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DOI：
10.3390/en15061958
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发表时间：
2022-03
期刊：
影响因子：
3.2
通讯作者：
Olukunle O. Owolabi;D. Sunter
中科院分区：
文献类型：
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作者：
Olukunle O. Owolabi;D. Sunter
停电预测对于规划电力系统响应、恢复和维护工作是重要的。重要的是，公用事业管理人员要了解停电对当地配电基础设施的影响，以便制定适当的维护和恢复措施。文献中的停电预测模型通常在范围上受到限制，通常针对与停电事件相关的极端天气进行建模。虽然这些模型足以预测恶劣天气事件引起的大范围停电，但它们可能无法捕获更频繁的非天气相关停电（NWO）。在这项研究中，我们探索NWO的时间序列模型，结合国家的最先进的技术，利用先知模型在贝叶斯优化和分层预测。在定义了NWO（非天气停电计数指数，NWOCI）的稳健指标后，分别利用Kats和Prophet中的高级预处理和预测技术构建了时间序列预测模型，并使用六年的每日州级和县级停电数据进行了测试在马萨诸塞州（MA）。我们开发了一个预言家模型与贝叶斯真Parzen估计优化（EST-TPE）使用国家级的停电数据和一个分层的EST-Bottom-Up模型使用县级数据。我们发现，这些预测模型优于其他贝叶斯和层次模型组合的先知和季节性自回归综合移动平均（SARIMA）模型预测NWOCI在县和州一级。我们的时间序列趋势分解揭示了一个令人担忧的趋势，在MA的NWO的增长。最后，我们讨论这些意见和可能的建议，以减轻NWO。
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.