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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution.
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios
With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifying its uncertainty and variability.
Stochastic Virtual Battery Modeling of Uncertain Electrical Loads Using Variational Autoencoder
Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads.
Systems and Control Systems and Control
Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder
Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery.
Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems
Traditional classifier based method does not perform well, because of the inherent difficulty of detecting system level faults for closed loop dynamical system.
