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AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting

arXiv 2503.24019 ↗First posted 31 Mar 2025stat.ML cs.LG stat.APcited by 0

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Abstract (arXiv)

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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References · 12

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
  3. 2020Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learningcited by 174
  4. 2023Neural Architecture Search: Insights from 1000 Paperscited by 139
  5. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 30
  6. 2020Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in Francecited by 23
  7. 2023An algorithmic framework for the optimization of deep neural networks architectures and hyperparameterscited by 6
  8. 2024Automated Deep Learning for Load Forecastingcited by 3
  9. 2024A Bandit Approach with Evolutionary Operators for Model Selectioncited by 2
  10. 2024Efficient mid-term forecasting of hourly electricity load using generalized additive modelscited by 2
  11. 2023Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive modelscited by 1
  12. 2024Automated Spatio-Temporal Weather Modeling for Load Forecastingcited by 1

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