Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of the wind power output and effectively improve a system's wind power accommodation. However, the highdimensional historical wind speed information should be taken into account in the wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Furthermore, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. The experiments show that it reduces the information loss. Thus, the model has a higher forecast accuracy than the traditional model. INDEX TERMS Short-term wind speed forecast, feature selection, feature reduction, low information loss, variational mode decomposition, singular value decomposition. The associate editor coordinating the review of this manuscript and approving it for publication was Shuaihu Li. which makes the safe and reliable operation of a power grid enormously challenging and restricts the development of wind power. Accurate and efficient wind speed forecast (WSF) can reduce the negative impact of wind power uncertainty [4], [5]. WSF methods can generally be divided into physical methods [6], [7], statistical methods [8]-[11], artificial intelligence (AI) methods [12]-[17], and others. The physical method is based on a numerical weather prediction (NWP) model, which uses a series of meteorological data (wind direction, temperature, and humidity) and terrain information to establish WSFM [6], [7]. Unlike physical methods, statistical methods only use historical
Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.
Wind speed forecasting is an indispensable part of wind energy assessment and power system scheduling. In the modeling of wind speed forecasting, there are problems of insufficiency of the high input feature dimension, weak pertinence of the model and a lack of consideration about the redundancy between features. To address these problems, a short-term wind speed forecast method based on low redundancy feature selection is proposed. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to pretreat the wind speed data to reduce the randomness and fluctuation of wind speed data. Secondly, conditional mutual information (CMI) is used to analyze the correlation between the input features on different predicted days and wind speed series. The feature order based on conditional mutual information is used to reduce the redundancy between candidate features and establish subsets with candidate features. After that, according to different candidate feature subsets of different predicted days, the outlier-robust extreme learning machine (ORELM) is used to carry out the forward feature selection and obtain optimal feature subsets for different predicted days. Finally, the optimal prediction model is constructed by using the optimal feature subset and the short-term wind speed forecasting is carried out. The validity and advance of the new method are verified by measured data through comparison experiments.
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