Automated Deep Learning for load forecasting

Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère

Keywords: Automated Deep Learning, Neural Architecture Search, Hyperparamer Optimization, Features Selection, Load Forecasting

TL;DR: An automated deep learning framework designed for load forecasting.

Abstract: 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\footnote{https://dragon-tutorial.readthedocs.io/en/latest/} 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.

Submission Checklist: Yes

Broader Impact Statement: Yes

Paper Availability And License: Yes

Code Of Conduct: Yes

Optional Meta-Data For Green-AutoML: This blue field is just for structuring purposes and cannot be filled.

Steps For Environmental Footprint Reduction During Development: We used a supercomputer called CRONOS hosted in France, which has a low carbon footprint compare to other supercomputers.

CPU Hours: 1

GPU Hours: 336

TPU Hours: 0

Evaluation Metrics: No

Estimated CO2e Footprint: 3.36

Submission Number: 8