A new hybrid framework for probabilistic wind speed prediction using deep feature selection and multi-error modification. (1st November 2019)

• Record Type: Journal Article
• Authors: Jiang, Yan; Zhao, Ning; Peng, Liuliu; Liu, Shuoyu
• Is Part Of: Energy conversion and management. Volume 199(2019)
• Publication Date: 2019-11-01
• Subjects: Reliable wind speed prediction -- Probabilistic prediction -- Enhanced variational mode decomposition -- Deep feature selection -- Multi-error modification; 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.2019.111981
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Enhanced variational mode decomposition with adaptive level number is proposed. Deep feature selection for reducing forecasting difficulty is developed. Multi-error modification is generated to explain different properties in the data. Based on above techniques, the proposed probabilistic framework is offered. Case studies show hybrid method performs well in reliability and stability. Abstract: Wind speed prediction is critical for the wind power exploitation and management due to their strong positive correlation. However, since natural wind owns the high uncertainty and nonstationarity, reliable wind speed prediction is generally difficult to be realized. The objective of this study is to develop a new method for accurately explaining these characteristics and then providing reliable prediction. To this end, a novel probabilistic forecasting framework using enhanced variational mode decomposition, deep feature selection and multi-error modification is proposed. Concretely, the raw data is preprocessed firstly by the enhanced variational mode decomposition where its decomposition level number can be adaptively optimized by the combination of Hilbert transform and empirical model decomposition. Then, an innovative feature selection method which is hybrid of Kullback-Leibler divergence, Gram-Schmidt orthogonal and sample entropy is developed to conduct deep feature identification. Finally, after the deterministic prediction is performed by least square support vector machine, a post-processing multi-error modification is generated to implement the probabilistic prediction. In this method, four probabilistic models including kernel density estimation, univariate conditional kernel density estimation, generalized autoregressive conditional heteroscedasticity and their hybrid model are employed to capture different properties embedded in the error component. Four case studies based on the measured data are carried out. Systematic assessment results show the proposed method has well-pleasing forecasting capability and may be more suitable for the data with higher nonstationarity and non-Gaussianity. For example, the coverage width-based criterion of the proposed method in terms of data collection 4 is 0.261, while those from data collections 1, 2 and 3 are 0.343, 0.295 and 0.282, respectively.