Title :

Automated Deep Learning for Load Forecasting

Author(s) :

Keisler, Julie [Auteur]
EDF R&D [EDF R&D]
Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie [EDF R&D OSIRIS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Claudel, Sandra [Auteur]
Cabriel, Gilles [Auteur]
Brégère, Margaux [Auteur]

Scientific editor(s) :

PMLR

Conference title :

Third International Conference on Automated Machine Learning

City :

Paris

Country :

France

Start date of the conference :

2024-09-09

Book title :

Proceedings of the Third International Conference on Automated Machine Learning

English keyword(s) :

Automated Deep Learning
Neural Architecture Search
Hyperparameters Optimization
Features selection
Load Forecasting

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseau de neurones [cs.NE]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL