[PDF][PDF] Beatgan: Anomalous rhythm detection using adversarially generated time series.
Given a large-scale rhythmic time series containing mostly normal data segments (or
'beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For …
'beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For …
Time series anomaly detection with adversarial reconstruction networks
Time series data naturally exist in many domains including medical data analysis,
infrastructure sensor monitoring, and motion tracking. However, a very small portion of …
infrastructure sensor monitoring, and motion tracking. However, a very small portion of …
Time series anomaly detection for smart grids: A survey
With the rapid increase in the integration of renewable energy generation and the wide
adoption of various electric appliances, power grids are now faced with more and more …
adoption of various electric appliances, power grids are now faced with more and more …
BCAuth: Physical layer enhanced authentication and attack tracing for backscatter communications
Backscatter communication (BC) enables ultra-low-power communications and allows
devices to harvest energy simultaneously. But its practical deployment faces severe security …
devices to harvest energy simultaneously. But its practical deployment faces severe security …
Energy forecasting with robust, flexible, and explainable machine learning algorithms
Energy forecasting is crucial in scheduling and planning future electric load, so as to
improve the reliability and safeness of the power grid. Despite recent developments of …
improve the reliability and safeness of the power grid. Despite recent developments of …
PMU missing data recovery using tensor decomposition
The paper proposes a new approach for the recovery of missing data from phasor
measurement units (PMUs). The approach is based on the application of tensor …
measurement units (PMUs). The approach is based on the application of tensor …
Dynamic graph-based anomaly detection in the electrical grid
Given sensor readings over time from a power grid, how can we accurately detect when an
anomaly occurs? A key part of achieving this goal is to use the network of power grid …
anomaly occurs? A key part of achieving this goal is to use the network of power grid …
Forecasting the evolution of hydropower generation
Hydropower is the largest renewable energy source for electricity generation in the world,
with numerous benefits in terms of: environment protection (near-zero air pollution and …
with numerous benefits in terms of: environment protection (near-zero air pollution and …
eForecaster: Unifying electricity forecasting with robust, flexible, and explainable machine learning algorithms
Electricity forecasting is crucial in scheduling and planning of future electric load, so as to
improve the reliability and safeness of the power grid. Despite recent developments of …
improve the reliability and safeness of the power grid. Despite recent developments of …
[PDF][PDF] NeuCast: Seasonal Neural Forecast of Power Grid Time Series.
In the smart power grid, short-term load forecasting (STLF) is a crucial step in scheduling
and planning for future load, so as to improve the reliability, cost, and emissions of the power …
and planning for future load, so as to improve the reliability, cost, and emissions of the power …