LAPSE:2019.1638
Published Article
LAPSE:2019.1638
Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method
Keke Wang, Dongxiao Niu, Lijie Sun, Hao Zhen, Jian Liu, Gejirifu De, Xiaomin Xu
December 16, 2019
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.
Keywords
complementary ensemble empirical mode decomposition, hybrid forecasting model, improved extreme learning machine with kernel, sample entropy, wind power forecasting
Suggested Citation
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. (2019). LAPSE:2019.1638
Author Affiliations
Wang K: School of Economics and Management, North China Electric Power University, Beijing 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China
Niu D: School of Economics and Management, North China Electric Power University, Beijing 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China
Sun L: School of Economics and Management, North China Electric Power University, Beijing 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China
Zhen H: School of Economics and Management, North China Electric Power University, Beijing 102206, China; Beijing Key Laboratory of New Energy and Low-Carbon Development, Beijing 102206, China
Liu J: North China Power Dispatching and Control Centre, Beijing 100053, China
De G: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xu X: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Journal Name
Processes
Volume
7
Issue
11
Article Number
E843
Year
2019
Publication Date
2019-11-11
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7110843, Publication Type: Journal Article
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LAPSE:2019.1638
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doi:10.3390/pr7110843
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Dec 16, 2019
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Calvin Tsay
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