Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation. (15th December 2021)

• Record Type: Journal Article
• Authors: Jiang, Zheyong; Che, Jinxing; Wang, Lina
• Is Part Of: Energy conversion and management. Volume 250(2021)
• Publication Date: 2021-12-15
• Subjects: Empirical mode decomposition -- Spatial correlation -- Vector autoregressive -- Wind speed forecasting; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2021.114919
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A hybrid wind speed forecasting method based on EMD-VAR model and spatial correlation is proposed. Empirical mode decomposition can simplify the complex data structure. Information enhancement for wind speed forecast modelling is achieved by spatial correlation and VAR. The superiority of EMD-VAR model is verified in a wind farm in China. Abstract: Accurate wind speed forecasting is conducive to reduce the risk of power system from wind power uncertainty, which is of great significance to power system operation. However, it's very difficult to achieve satisfactory results in wind speed forecasting due to the complex fluctuation characteristics of wind speed series. This study proposes a novel EMD-VAR wind speed forecasting model based on the wind speed data of multiple adjacent measuring points with high correlation. To achieve better experimental results with high accuracy and strong stability, multiple adjacent spatial sites are used to balance the information and variance of the forecasting model. Empirical mode decomposition (EMD) methods are utilized to remove the noise in the original data and multiple intrinsic mode function (IMF) components are obtained. For each IMF component, the corresponding vector autoregressive (VAR) model is established for the spatial groups. The final forecasting result is obtained by summarizing the forecasting results of all the IMF components. To validate the accuracy and stability of the proposed model, wind speed data sets in four seasons are used for experimental prediction. Experiments show that this method can effectively improve the accuracy and guarantee the reliability of wind speed forecasting in each season.