Generalizing Renewable Energy Forecasting Using Automatic Feature Selection and Combination

Dennis van Der Meer , Simon Camal (1) , Georges Kariniotakis

1 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques

Spatially aggregating renewable power plants is beneficial when participating in electricity markets. In this context, a substantial number of features is available from various data sources. In machine learning, feature selection is common so as to relieve the curse of dimensionality and avoid overfitting. However, there is no guarantee that the selected features result in reliable forecasts and post-processing can therefore be valuable. In this study, we combine model agnostic feature selection with linear and nonlinear probabilistic forecast combination techniques. Moreover, the filters automatically compute the weights for our analog ensemble (AnEn) forecast model. We verify our model chain by generating intra-day forecasts of the aggregated output of 20 photovoltaic power plants using 831 input features in total. We show that the collection of filters selects a heterogeneous feature set but that each individual AnEn-filter combination results in underdispersed forecasts, which is efficiently remedied by the forecast combination techniques.

Mots clés: Filtering; forecast combination; highdimensional data; virtual power plant; probabilistic forecasts

Domaines: Machine Learning [stat.ML]; Optimisation et contrôle [math.OC]; Applications [stat.AP]

Dennis van Der Meer, Simon Camal, Georges Kariniotakis. Generalizing Renewable Energy Forecasting Using Automatic Feature Selection and Combination. 2022. ⟨hal-03638914v1⟩