  The key component in forecasting demand and consumption of resources in a
supply network is an accurate prediction of real-valued time series. Indeed,
both service interruptions and resource waste can be reduced with the
implementation of an effective forecasting system. Significant research has
thus been devoted to the design and development of methodologies for short term
load forecasting over the past decades. A class of mathematical models, called
Recurrent Neural Networks, are nowadays gaining renewed interest among
researchers and they are replacing many practical implementation of the
forecasting systems, previously based on static methods. Despite the undeniable
expressive power of these architectures, their recurrent nature complicates
their understanding and poses challenges in the training procedures. Recently,
new important families of recurrent architectures have emerged and their
applicability in the context of load forecasting has not been investigated
completely yet. In this paper we perform a comparative study on the problem of
Short-Term Load Forecast, by using different classes of state-of-the-art
Recurrent Neural Networks. We test the reviewed models first on controlled
synthetic tasks and then on different real datasets, covering important
practical cases of study. We provide a general overview of the most important
architectures and we define guidelines for configuring the recurrent networks
to predict real-valued time series.
