Abstract
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
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 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.
INDEX TERMS deep learning, generation forecasting, load forecasting, neural networks, probabilistic
methods, renewable power
methods able to incorporate uncertainty estimations in predictions. This paper aims to extend the literature
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 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.
INDEX TERMS deep learning, generation forecasting, load forecasting, neural networks, probabilistic
methods, renewable power
