Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model

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@Article{JIANG:2020:ECM,

• author = "Yan Jiang and Shuoyu Liu and Ning Zhao and Jingzhou Xin and Bo Wu",
• title = "Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model",
• journal = "Energy Conversion and Management",
• volume = "220",
• pages = "113076",
• year = "2020",
• ISSN = "0196-8904",
• DOI = "doi:10.1016/j.enconman.2020.113076",
• URL = "http://www.sciencedirect.com/science/article/pii/S0196890420306208",
• keywords = "genetic algorithms, genetic programming, Short-term wind speed prediction, Time varying filter-based empirical mode decomposition, Group method of data handling neural network, Nonlinear residuals, Selective prediction",
• abstract = "The realization of precise and reliable short-term wind speed prediction is extremely essential to wind power development, especially for its integration into traditional grid system. For this purpose, this study develops a novel forecasting method based on time varying filter-based empirical mode decomposition, auto-regressive integrated moving average model and group method of data handling-based hybrid model. This method mainly contains four individual steps for grasping the major behavioral characteristics of wind speed data. The first step adopts time varying filter-based empirical mode decomposition to handle the nonlinearity and nonstationarity of the raw wind speed data by decomposing them into a number of subseries with more stability and regularity. Then, auto-regressive integrated moving average model is applied to depict the linear characteristic hidden in the data. For the above modeling errors (i.e., the nonlinear residuals), the third step employs three nonlinear models with different action mechanisms (i.e., least square support vector machine, genetic programming algorithm and spatio-temporal radial basis function neural network) to systematically capture their complex nonlinear features. Finally, group method of data handling neural network is used to combine these nonlinear models and perform the selective prediction, where the involved models and their weights could be determined automatically. Four groups of the measured wind speed datasets with two different time intervals are used to assess the performance of the proposed method. The experimental results indicate it outperforms the other compared models and may have great potential for the practical application in power system",

}

Genetic Programming entries for Yan Jiang Shuoyu Liu Ning Zhao Jingzhou Xin Bo Wu

Citations