Short-term wind power forecasting using KNN, SVR, and linear regression with high-resolution SCADA data

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Abstract
Short-term wind power forecasting supports grid stability despite wind’s intermittency, but deep learning (DL) models often require excessive computation, hindering real-time deployment. This study compares three classical regression models, linear regression (LR), k-nearest neighbors (KNNs), and support vector regression (SVR) using 10-minute SCADA data. The data retained raw variability, with only negative power values set to zero. Hyperparameters of KNN and SVR were optimized using GridSearchCV with time series cross-validation. Optimized SVR achieved superior performance (MAE = 183.93 kW, RMSE = 387.64 kW, R 2 = 0.91, MAPE = 144.88%, MdAE = 84.44 kW), outperforming KNN (R 2 = 0.79) and LR (R 2 = 0.91 but higher errors). Scatter plots and residuals highlighted SVR’s ability to handle nonlinearities. Results suggest that SVR is a viable candidate SVR for low-resource, real-time forecasting in renewables.

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