Automated Deep Learning for Load Forecasting

TL;DR

This paper introduces EnergyDragon, an AutoDL framework that automatically designs deep neural networks for electricity load forecasting, outperforming existing methods by effectively selecting features and optimizing architectures.

Contribution

We developed EnergyDragon, an AutoDL framework that enhances load forecasting by automating feature selection and neural network optimization, demonstrating superior performance on real data.

Findings

EnergyDragon outperforms state-of-the-art load forecasting methods.

Automated feature selection improves model accuracy.

Optimized neural networks achieve better results than existing AutoDL approaches.

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 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.