ExARNN builds a continuous weather signal with a neural controlled differential equation and uses it to generate time-varying parameters for a recurrent network, reporting improved load-forecasting accuracy on two datasets.
A review on the selected applications of forecasting models in renewable power systems,
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ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
ExARNN builds a continuous weather signal with a neural controlled differential equation and uses it to generate time-varying parameters for a recurrent network, reporting improved load-forecasting accuracy on two datasets.