  Probabilistic load forecasting (PLF) is a key component in the extended
tool-chain required for efficient management of smart energy grids. Neural
networks are widely considered to achieve improved prediction performances,
supporting highly flexible mappings of complex relationships between the target
and the conditioning variables set. However, obtaining comprehensive predictive
uncertainties from such black-box models is still a challenging and unsolved
problem. In this work, we propose a novel PLF approach, framed on Bayesian
Mixture Density Networks. Both aleatoric and epistemic uncertainty sources are
encompassed within the model predictions, inferring general conditional
densities, depending on the input features, within an end-to-end training
framework. To achieve reliable and computationally scalable estimators of the
posterior distributions, both Mean Field variational inference and deep
ensembles are integrated. Experiments have been performed on household
short-term load forecasting tasks, showing the capability of the proposed
method to achieve robust performances in different operating conditions.
