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Article

Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China
3
North China Power Dispatching and Control Centre, Beijing 100053, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(11), 843; https://doi.org/10.3390/pr7110843
Received: 20 October 2019 / Revised: 4 November 2019 / Accepted: 6 November 2019 / Published: 11 November 2019
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting. View Full-Text
Keywords: wind power forecasting; hybrid forecasting model; complementary ensemble empirical mode decomposition; sample entropy; improved extreme learning machine with kernel wind power forecasting; hybrid forecasting model; complementary ensemble empirical mode decomposition; sample entropy; improved extreme learning machine with kernel
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MDPI and ACS Style

Wang, K.; Niu, D.; Sun, L.; Zhen, H.; Liu, J.; De, G.; Xu, X. Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method. Processes 2019, 7, 843. https://doi.org/10.3390/pr7110843

AMA Style

Wang K, Niu D, Sun L, Zhen H, Liu J, De G, Xu X. Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method. Processes. 2019; 7(11):843. https://doi.org/10.3390/pr7110843

Chicago/Turabian Style

Wang, Keke, Dongxiao Niu, Lijie Sun, Hao Zhen, Jian Liu, Gejirifu De, and Xiaomin Xu. 2019. "Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method" Processes 7, no. 11: 843. https://doi.org/10.3390/pr7110843

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