  Integrating wind power into the grid is challenging because of its random
nature. Integration is facilitated with accurate short-term forecasts of wind
power. The paper presents a spatio-temporal wind speed forecasting algorithm
that incorporates the time series data of a target station and data of
surrounding stations. Inspired by Compressive Sensing (CS) and
structured-sparse recovery algorithms, we claim that there usually exists an
intrinsic low-dimensional structure governing a large collection of stations
that should be exploited. We cast the forecasting problem as recovery of a
block-sparse signal $\boldsymbol{x}$ from a set of linear equations
$\boldsymbol{b} = A\boldsymbol{x}$ for which we propose novel structure-sparse
recovery algorithms. Results of a case study in the east coast show that the
proposed Compressive Spatio-Temporal Wind Speed Forecasting (CST-WSF) algorithm
significantly improves the short-term forecasts compared to a set of
widely-used benchmark models.
