  Accurate forecasting of electricity consumption is essential to ensure the
performance and stability of the grid, especially as the use of renewable
energy increases. Forecasting electricity is challenging because it depends on
many external factors, such as weather and calendar variables. While
regression-based models are currently effective, the emergence of new
explanatory variables and the need to refine the temporality of the signals to
be forecasted is encouraging the exploration of novel methodologies, in
particular deep learning models. However, Deep Neural Networks (DNNs) struggle
with this task due to the lack of data points and the different types of
explanatory variables (e.g. integer, float, or categorical). In this paper, we
explain why and how we used Automated Deep Learning (AutoDL) to find performing
DNNs for load forecasting. We ended up creating an AutoDL framework called
EnergyDragon by extending the DRAGON package and applying it to load
forecasting. EnergyDragon automatically selects the features embedded in the
DNN training in an innovative way and optimizes the architecture and the
hyperparameters of the networks. We demonstrate on the French load signal that
EnergyDragon can find original DNNs that outperform state-of-the-art load
forecasting methods as well as other AutoDL approaches.
