Automated Deep Learning for Load Forecasting
This addresses the challenge of accurate load forecasting for grid stability, especially with renewable energy integration, though it appears incremental as it extends an existing AutoDL package.
The paper tackled the problem of electricity load forecasting by developing an Automated Deep Learning (AutoDL) framework called EnergyDragon, which automatically selects features and optimizes neural network architectures, resulting in DNNs that outperform state-of-the-art methods and other AutoDL approaches on the French load signal.
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.