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Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

机译：小波变换在时空风速预测中利用互连稀疏性

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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. (C) 2015 Elsevier Ltd. All rights reserved.
机译：将可再生能源整合到电网中对于实现预期的可持续能源未来至关重要。然而，可再生能源的随机性和间歇性特征为将这些资源大规模整合到现有电网中提出了挑战。准确的风能预报有助于实现可靠的可再生能源集成。在本文中，我们提出了一种新颖的风速预测方法，该方法首先利用小波变换（WT）将风速数据分解为更多的平稳分量，然后在每个子序列上使用时空模型来合并时空信息。在每个子系列上建议的时空预测方法是基于这样的假设，即在一组气象站中，时间序列数据之间通常存在固有的低维结构。我们的方法受到压缩感知（CS）和结构稀疏恢复算法的启发。基于详细的案例研究，我们表明，基于利用大量气象站之间的相关性稀疏性并分解时间序列以进行更高精度的预测的提议方法，与时空和时空相比，大大改善了短期预测基准方法。 （C）2015 Elsevier Ltd.保留所有权利。

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