  Short-term load forecasting (STLF) is challenging due to complex time series
(TS) which express three seasonal patterns and a nonlinear trend. This paper
proposes a novel hybrid hierarchical deep learning model that deals with
multiple seasonality and produces both point forecasts and predictive intervals
(PIs). It combines exponential smoothing (ES) and a recurrent neural network
(RNN). ES extracts dynamically the main components of each individual TS and
enables on-the-fly deseasonalization, which is particularly useful when
operating on a relatively small data set. A multi-layer RNN is equipped with a
new type of dilated recurrent cell designed to efficiently model both short and
long-term dependencies in TS. To improve the internal TS representation and
thus the model's performance, RNN learns simultaneously both the ES parameters
and the main mapping function transforming inputs into forecasts. We compare
our approach against several baseline methods, including classical statistical
methods and machine learning (ML) approaches, on STLF problems for 35 European
countries. The empirical study clearly shows that the proposed model has high
expressive power to solve nonlinear stochastic forecasting problems with TS
including multiple seasonality and significant random fluctuations. In fact, it
outperforms both statistical and state-of-the-art ML models in terms of
accuracy.
