A Time-Series Distribution Test System Based on Real Utility Data
In this paper, we provide a time-series distribution test system. This test system is a fully observable distribution grid in Midwest U.S. with smart meters (SM) installed at all end users. Our goal is to share a real U.S. distribution grid model without modification. This grid model is comprehensive and representative since it consists of both overhead lines and underground cables, and it has standard distribution grid components such as capacitor banks, line switches, substation transformers with load tap changer and secondary distribution transformers. An important uniqueness of this grid model is it has one-year smart meter measurements at all nodes, thus bridging the gap between existing test feeders and quasi-static time-series based distribution system analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Causal Discovery using Model Invariance through Knockoff Interventions
Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causality in nonlinear multivariate systems of …
Causal DiscoverymodelTime SeriesTime Series AnalysisCANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift
Multivariate time-series anomaly detection (MTSAD) aims to identify deviations from normality in multivariate time-series and is critical in real-world applications. However, in real-world deployments, distribution shift…
Test-time AdaptationAnomaly DetectionConditional independence testing with a single realization of a multivariate nonstationary nonlinear time series
Identifying relationships among stochastic processes is a key goal in disciplines that deal with complex temporal systems, such as economics. While the standard toolkit for multivariate time series analysis has many adva…
Causal DiscoveryTime SeriesTime Series AnalysisVariable SelectionWhen Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection
Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal…
Anomaly DetectionTest-time AdaptationTime SeriesTime Series Anomaly DetectionTwo-Sample Testing for Event Impacts in Time Series
In many application domains, time series are monitored to detect extreme events like technical faults, natural disasters, or disease outbreaks. Unfortunately, it is often non-trivial to select both a time series that is …
Event DetectionTime SeriesTime Series AnalysisTwo-sample testing+1