Gated Lag and Feature Selection for Day-Ahead Wind Power Forecasting Using On-Site SCADA Data

Day-ahead wind power forecasting is often limited to on-site Supervisory Control and Data Acquisition (SCADA) datasets without Numerical Weather Prediction (NWP) information. In this regime, practitioners extend autoregressive windows over many variables, so the input size grows with both features and lags. Many lag–feature pairs are redundant, increasing the training time and overfitting risk. A lightweight, differentiable joint gate over the lag–feature plane trained with a temperature-annealed sigmoid is proposed. Sparsity is induced by capped penalties that (i) bound the total open mass to the top-M features and (ii), within each selected feature, bound the mass to the top-k lags. An additional budget-aware off-state term pushes unused logits negative in proportion to the excess density over the (M×k) budget. A lightweight, per-feature softmax pooling head supplies the forecasting loss during selection. After training, the learned probabilities are converted into compact, non-contiguous lag–feature subsets (top-M features; per-feature top-k lags) and reused by downstream predictors. Tests on the Offshore Renewable Energy (ORE) Catapult Platform for Operational Data (POD) from the Levenmouth Demonstration Turbine (LDT) dataset show that the joint gate reduces the input dimensionality and training time while improving accuracy and stability relative to Pearson’s correlation, mutual information, and cross-correlation function selectors.