[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 …
Robust power flow and three-phase power flow analyses
Robust simulation is essential for reliable operation and planning of transmission and
distribution power grids. At present, disparate methods exist for steady-state analysis of the …
distribution power grids. At present, disparate methods exist for steady-state analysis of the …
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 …
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 …
Classical and contemporary approaches to big time series forecasting
Time series forecasting is a key ingredient in the automation and optimization of business
processes: in retail, deciding which products to order and where to store them depends on …
processes: in retail, deciding which products to order and where to store them depends on …
Optimal sampling designs for multidimensional streaming time series with application to power grid sensor data
Optimal sampling designs for multidimensional streaming time series with application to power
grid sensor data Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4, 3195–3215 …
grid sensor data Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 4, 3195–3215 …
HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows
We present HydroFlow, a novel deep generative model for predicting the electricity
generation demand of large‐scale hydropower stations. HydroFlow uses a latent stochastic …
generation demand of large‐scale hydropower stations. HydroFlow uses a latent stochastic …