38 citations · 52 across the 11 of their papers we have counts for
11 papers
AI Foundation Model for Time Series with Innovations Representation
Lang Tong, Xinyi Wang
This paper introduces an Artificial Intelligence (AI) foundation model for time series in engineering applications, where causal operations are required for real-time monitoring an…
Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply
Minjae Jeon, Lang Tong, Qing Zhao
We investigate the problem of serving deferrable and nondeferrable electric demands with colocated stochastic supply and grid-imported electricity. Deferrable demands arrive random…
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies
Lang Tong, Xinyi Wang, Qing Zhao
Purpose:This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. Leveragi…
Probabilistic Forecasting of Real-Time Electricity Market Signals via Interpretable Generative AI
Xinyi Wang, Qing Zhao, Lang Tong
This paper introduces a generative AI approach to probabilistic forecasting of real-time electricity market signals, including locational marginal prices, interregional price sprea…
Generative Probabilistic Time Series Forecasting and Applications in Grid Operations
Xinyi Wang, Lang Tong, Qing Zhao
Generative probabilistic forecasting produces future time series samples according to the conditional probability distribution given past time series observations. Such techniques…
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
Xinyi Wang, Meijen Lee, Qing Zhao +1
Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical i…
Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach
Xinyi Wang, Mei-jen Lee, Qing Zhao +1
We consider novelty detection in time series with unknown and nonparametric probability structures. A deep learning approach is proposed to causally extract an innovations sequence…
As Easy as ABC: Adaptive Binning Coincidence Test for Uniformity Testing
Sudeep Salgia, Qing Zhao, Lang Tong
We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous dist…
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection
Xinyi Wang, Lang Tong
An innovations sequence of a time series is a sequence of independent and identically distributed random variables with which the original time series has a causal representation.…
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems
Kursat Rasim Mestav, Xinyi Wang, Lang Tong
A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-…
Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data
Xinyi Wang, Yilu Liu, Lang Tong
A data compression system capable of providing real-time streaming of high-resolution continuous point-on-wave (CPOW) and phasor measurement unit (PMU) measurements is proposed. Re…