HyperEnergy, a hypernetwork that learns LSTM weights with a learnable polynomial/RBF kernel, reports lower 24-hour-ahead energy forecast errors than 10 baselines on most of 10 building datasets.
Application of decoupled ARMA model to modal identification of linear time- varying system based on the ICA and assumption of “short-time linearly varying
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Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types
HyperEnergy, a hypernetwork that learns LSTM weights with a learnable polynomial/RBF kernel, reports lower 24-hour-ahead energy forecast errors than 10 baselines on most of 10 building datasets.