Short-term wind power forecasting using nonnegative sparse coding

State-of-the-art statistical learning techniques are adapted in this contribution for real-time wind power forecasting. Spatio-temporal wind power outputs are modeled as a linear combination of “few” atoms in a dictionary. By exploiting geographical information of wind farms, a graph Laplacian-based regularizer encourages positive correlation of wind power levels of adjacent farms. Real-time forecasting is achieved by online nonnegative sparse coding with elastic net regularization. The resultant convex optimization problems are efficiently solved using a block coordinate descent solver. Numerical tests on real data corroborate the merits of the proposed approach, which outperforms competitive alternatives in forecasting accuracy.