Multi-step wind power forecast based on similar segments extracted by mathematical morphology
作者： J. L. Wu , T. Y. Ji , M. S. Li , Q. H. Wu
DOI: 10.1109/APPEEC.2014.7066041
关键词:
摘要: This paper proposes a pre-processing method to enhance the accuracy of wind power forecast. Instead using whole dataset indifferently for training, proposed only uses segments that share same pattern. In order search such in historical data, k-OCCO filter and weighted multi-resolution morphological gradient (MMG) are employed. Afterwards, forecast is conducted by least square support vector machine (LS-SVM) model, these training. Simulation studies carried out on data demonstrate advantage method, results have shown both stability LS-SVM model been improved introducing method.
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