|Title||Low-dimensional models in spatio-temporal wind speed forecasting|
|Publication Type||Conference Paper|
|Year of Publication||2015|
|Authors||Borhan M Sanandaji, Akin Tascikaraoglu, Kameshwar Poolla, Pravin Varaiya|
|Conference Name||2015 American Control Conference (ACC)|
|Conference Location||Chicago, IL, USA|
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 x from a set of linear equations b = Ax 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 (CSTWSF) algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmark models.