Probabilistic day-ahead energy price forecast by a Mixture Density Recurrent Neural Network

Probabilistic electricity price forecast (EPF) systems represent a fundamental tool to achieve robust production scheduling and day-ahead bidding strategies. However, most EPF methods, including recently proposed deep learning based techniques, are still targeting point predictions, following the common Gaussian assumption. In this work, we propose a novel probabilistic EPF approach based on the integration of a Gaussian Mixture layer, parametrized by a Recurrent Neural Network with Gated Recurrent Units, including an L1-norm based feature selection mechanisms. The network is conceived to approximate general conditional price distributions through learning. Moreover, we developed a multi-hours prediction approach exploiting correlations and patters both in hourly and cross-hour contexts. Experiments have been performed on the Italian market dataset, showing the capability of the proposed method to achieve accurate out-of-sample predictions while providing explicit uncertainty indications supporting enhanced decision making.