Probabilistic electric load forecasting through Bayesian Mixture Density Networks
This work aims to improve the reliability and comprehensiveness of predictive uncertainties for electric load forecasting, which is crucial for efficient management of smart energy grids.
This paper addresses probabilistic electric load forecasting by proposing a novel approach based on Bayesian Mixture Density Networks. The method integrates Mean Field variational inference and deep ensembles to capture both aleatoric and epistemic uncertainties, demonstrating robust performance in household short-term load forecasting.
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.