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.

翻译：电力消耗的精确预测对于保障电网性能和稳定性至关重要，特别是在可再生能源使用日益增长的背景下。电力预测具有挑战性，因为它依赖于天气和日历变量等众多外部因素。虽然基于回归的模型目前十分有效，但新的解释变量的出现以及对预测信号时间尺度进行精细化处理的需求，正推动着对新颖方法论（尤其是深度学习模型）的探索。然而，深度神经网络由于数据点匮乏以及解释变量类型多样（例如整数、浮点数或类别变量），在该任务上面临困难。本文阐述了为何以及如何利用自动化深度学习来发现用于负荷预测的高性能深度神经网络。我们通过扩展DRAGON包，最终构建了一个名为EnergyDragon的自动化深度学习框架，并将其应用于负荷预测。EnergyDragon以创新方式自动选择嵌入深度神经网络训练的特征，并优化网络架构与超参数。我们在法国负荷信号上证明，EnergyDragon能够发现创新的深度神经网络，其性能优于最先进的负荷预测方法及其他自动化深度学习方法。