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

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.