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.
Highlights
As a clean renewable energy, wind energy is regarded as a good alternative to deal with environmental problems and energy crises [1,2]
empirical mode decomposition-permutation entropy (EEMD-Permutation Entropy (PE))-least squares support vector machine model (LSSVM) were positive, which shows that the performance of the proposed model was worse than the Ensemble Empirical Mode Decomposition (EEMD)-PE-LSSVM model
In (1–3)-step-ahead forecasting, the proposed method had a positive value of ξ normalized mean absolute of errors (NMAE)(%) compared with EEMD-PE-LSSVM, which shows that the proposed model was worse than the EEMD-PE-LSSVM
Summary
As a clean renewable energy, wind energy is regarded as a good alternative to deal with environmental problems and energy crises [1,2]. According to a report published by the World. Wind Energy Association (WWEA), worldwide wind capacity reached 54 GW by the end of 2017, with a growth rate of 11.8% [3]. The intermittent nature of wind power generation has posed a big challenge for maximizing the utilization of the wind power industry [4]. It is of practical significance to optimize the wind power prediction algorithm and make it more suitable for the operation and wind conditions of a specific wind farm.
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