A Temporal Neural Network Model for Probabilistic Multi-Period Forecasting of Distributed Energy Resources

A Temporal Neural Network Model for Probabilistic Multi-Period Forecasting of Distributed Energy Resources

Blog Article

Probabilistic forecasts of electrical loads and photovoltaic generation provide a family of methods able to incorporate uncertainty estimations in predictions.This paper aims to extend the literature Backpack on these methods by proposing a novel deep-learning model based on a mixture of convolutional neural networks, transformer models and dynamic Bayesian networks.Further, the paper also illustrates how to utilize Stochastic Variational Inference for training output distributions that allow time series sampling, a possibility not given for most state-of-the-art methods which do not use distributions.On top of this, the model also proposes an encoder-decoder topology that uses matrix transposes in order to both train on the sequential and OREGANO-8 the feature dimension.The performance of the work is illustrated on both load and generation time series obtained from a site representative of distributed energy resources in Norway and compared to state-of-the-art methods such as long-short-term memory.

With a single-minute prediction resolution and a single-second computation time for an update with a batch size of 100 and a horizon of 24 hours, the model promises performance capable of real-time application.In summary, this paper provides a novel model that allows generating future scenarios for time series of distributed energy resources in real-time, which can be used to generate profiles for control problems under uncertainty.