用自动化算法优化电力需求预测模型，提升实时预测精度
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
- 将神经网络搜索方法改造用于在线GAM模型公式的自动选择
- 在法国短期电力需求预测中实现比传统方法更低的平均误差（1.2%）
- 适合需要快速响应的能源系统建模与智能电网应用
电力需求预测对保障供电与防止电网瘫痪至关重要。通过结合广义加性模型（GAM）与状态空间模型（Obst et al., 2021），可构建自适应（或在线）预测模型。然而，GAM的公式结构和状态空间模型的超参数需预先设定，且显著影响预测性能。本文提出使用Keisler（2025）开发的DRAGON工具包，原用于神经架构搜索，将其推广至在线GAM模型选择，定义了高效搜索空间（包括GAM公式与适应参数）。在法国短期电力需求预测任务中的应用表明，该方法能有效提升预测精度，验证了其有效性与实用性。
原文摘要 · Abstract (English)
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
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