Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform. (1st March 2016)

• Record Type: Journal Article
• Authors: Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin
• Is Part Of: Applied energy. Volume 165(2016)
• Page Start: 735
• Page End: 747
• Publication Date: 2016-03-01
• Subjects: Wind forecasting -- Compressive sensing -- Spatial correlation -- Wavelet Transform; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.12.082
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: We propose a spatio-temporal approach for wind speed forecasting. The method is based on a combination of Wavelet decomposition and structured-sparse recovery. Our analyses confirm that low-dimensional structures govern the interactions between stations. Our method particularly shows improvements for profiles with high ramps. We examine our approach on real data and illustrate its superiority over a set of benchmark models. Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.