  Management and efficient operations in critical infrastructure such as Smart
Grids take huge advantage of accurate power load forecasting which, due to its
nonlinear nature, remains a challenging task. Recently, deep learning has
emerged in the machine learning field achieving impressive performance in a
vast range of tasks, from image classification to machine translation.
Applications of deep learning models to the electric load forecasting problem
are gaining interest among researchers as well as the industry, but a
comprehensive and sound comparison among different architectures is not yet
available in the literature. This work aims at filling the gap by reviewing and
experimentally evaluating on two real-world datasets the most recent trends in
electric load forecasting, by contrasting deep learning architectures on short
term forecast (one day ahead prediction). Specifically, we focus on feedforward
and recurrent neural networks, sequence to sequence models and temporal
convolutional neural networks along with architectural variants, which are
known in the signal processing community but are novel to the load forecasting
one.
