A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Peng Lu, Lin Ye, Bohao Sun, Cihang Zhang, Yongning Zhao and Jingzhu Teng
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Peng Lu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Lin Ye: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Bohao Sun: China Electric Power Research Institute, Haidian District, Beijing 100192, China
Cihang Zhang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Yongning Zhao: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Jingzhu Teng: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Energies, 2018, vol. 11, issue 4, 1-23

Abstract: Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

Keywords: wind power prediction; ensemble empirical mode decomposition-permutation entropy (EEMD-PE); least squares support vector machine (LSSVM); heuristic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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