Abstract
Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides insights on how to reduce outages in the future, and how to improve the robustness of the underlying critical infrastructure systems. In this article, we propose a novel scalable segmentation with explanations framework to help experts understand such datasets. Our method, CnR (Cut-n-Reveal), first finds a segmentation of the outage sequences based on the temporal variations of the power outage failure process so as to capture major pattern changes. This temporal segmentation procedure is capable of accounting for both the spatial and temporal correlations of the underlying power outage process. We then propose a novel explanation optimization formulation to find an intuitive explanation of the segmentation such that the explanation highlights the culprit time series of the change in each segment. Through extensive experiments, we show that our method consistently outperforms competitors in multiple real datasets with ground truth. We further study real county-level power outage data from several recent hurricanes (Matthew, Harvey, Irma) and show that CnR recovers important, non-Trivial, and actionable patterns for domain experts, whereas baselines typically do not give meaningful results.
Original language | English |
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Article number | 53 |
Journal | ACM Transactions on Intelligent Systems and Technology |
Volume | 11 |
Issue number | 5 |
DOIs | |
State | Published - Sep 2020 |
Funding
This document has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This article is based on work partially supported by the NSF (Expeditions CCF-1918770, CAREER IIS-1750407, DGE-1545362, IIS-1633363), the NEH (HG-229283-15), ORNL, (H98230-14-C-0127), and a Facebook faculty gift. Authors’ addresses: N. Muralidhar, A. Tabassum, and N. Ramakrishnan, Virginia Tech; emails: {nik90, anikat1}@vt.edu, [email protected]; L. Chen, Pinterest; email: [email protected]; S. Chinthavali, Oak Ridge National Laboratory; email: [email protected]; B. A. Prakash, Georgia Institute of Technology; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2020 Association for Computing Machinery. 2157-6904/2020/07-ART53 $15.00 https://doi.org/10.1145/3394118
Keywords
- Multivariate time series
- spatio-Temporal segmentation