  In this paper, we propose a new short-term load forecasting (STLF) model
based on contextually enhanced hybrid and hierarchical architecture combining
exponential smoothing (ES) and a recurrent neural network (RNN). The model is
composed of two simultaneously trained tracks: the context track and the main
track. The context track introduces additional information to the main track.
It is extracted from representative series and dynamically modulated to adjust
to the individual series forecasted by the main track. The RNN architecture
consists of multiple recurrent layers stacked with hierarchical dilations and
equipped with recently proposed attentive dilated recurrent cells. These cells
enable the model to capture short-term, long-term and seasonal dependencies
across time series as well as to weight dynamically the input information. The
model produces both point forecasts and predictive intervals. The experimental
part of the work performed on 35 forecasting problems shows that the proposed
model outperforms in terms of accuracy its predecessor as well as standard
statistical models and state-of-the-art machine learning models.
