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
Wind power combination probability prediction can effectively describe the uncertainty of wind farm output power and reduce the negative impact of this uncertainty on grid dispatching and operation. However, there is a conflict between coverage and interval width in probability prediction that affects the construction of the optimal prediction model. To resolve the conflicts between different probabilistic evaluation indicators, this paper proposes a new method for wind power combination probability prediction based on area gray correlation decision. First, the original wind power output data is reconstructed using energy-optimized variational mode decomposition to reduce the randomness of the original wind power signal. Processed wind power output data is used to establish an input feature set containing 96-dimensional historical wind power output data. Then, different Gaussian process regression prediction models are established based on 10 covariance functions. The area gray correlation method is used to calculate the area gray correlation degree of the five evaluation indicators, and a comprehensive evaluation of multiple indicators is carried out to eliminate the conflict between indicators. Finally, the weights of the prediction results of the 10 GPR models are determined according to the area gray correlation closeness, and the prediction interval and mean are reconstructed by combining the calculation results of each model. Simulation results show that the model determined by the new method has a more reliable prediction interval and faster prediction speed and can therefore provide decision support for wind power probability prediction and for the safe and stable operation of wind power grid connections.
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