Low-dimensional models in spatio-temporal wind speed forecasting

By Borhan Molazem Sanandaji, A. Taşcıkaraoǧlu, Kameshwar Poolla, Pravin Pratap Varaiya. 2015

26 citations recorded by the source corpus.

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.

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1. Amir Ghaderi
2. Faezeh Ghaderi

Citations in the corpus

• Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, methodology): “More details about how other methods work are available in (Sanandaji et al., 2015), (Tascikaraoglu et al.”