Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Additive Models (GAM) with a State-Space model (Obst et al., 2021), leading to an adaptive (or online) model. A GAM is an over-parameterized linear model defined by a formula and a state-space model involves hyperparameters. Both the formula and adaptation parameters have to be fixed before model training and have a huge impact on the model's predictive performance. We propose optimizing them using the DRAGON package of Keisler (2025), originally designed for neural architecture search. This work generalizes it for automated online generalized additive model selection by defining an efficient modeling of the search space (namely, the space of the GAM formulae and adaptation parameters). Its application to short-term French electricity demand forecasting demonstrates the relevance of the approach

翻译：电力需求预测对于确保供需平衡至关重要，否则电网可能发生停电。通过将广义可加模型（GAM）与状态空间模型（Obst等人，2021）相结合，可以获得可靠的自适应（或在线）短期预测模型。GAM是一种由公式定义的过参数化线性模型，而状态空间模型则包含超参数。公式和自适应参数均需在模型训练前确定，且对模型的预测性能具有重大影响。我们提出使用Keisler（2025）开发的DRAGON包（原为神经架构搜索设计）来优化这些参数。本研究通过定义高效的搜索空间建模（即GAM公式与自适应参数的空间），将其推广至自动化在线广义可加模型选择。在法国短期电力需求预测中的应用验证了该方法的有效性。