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Found 6 papers, 3 papers with code
Transformer Training Strategies for Forecasting Multiple Load Time Series
We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients.
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information
Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly.
Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks
More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts.
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error.
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
In the present paper, we analyze the impact of data representations on the performance of Deep Neural Networks using energy time series forecasting.
pyWATTS: Python Workflow Automation Tool for Time Series
Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems.
