气候变化领域集成服务门户(CCPortal): Short-term wind speed prediction using an extreme learning machine model with error correction

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DOI	10.1016/j.enconman.2018.02.015
	Short-term wind speed prediction using an extreme learning machine model with error correction
	Wang, Lili; Li, Xin; Bai, Yulong
通讯作者	Li, X (通讯作者)
发表日期	2018
ISSN	0196-8904
EISSN	1879-2227
起始页码	239
结束页码	250
卷号	162
英文摘要	Wind speed forecasting is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speeds accurately is difficult. Aims at this challenge, a new hybrid model is proposed for short-term wind speed forecasting, where the short-term forecasting period is ten minutes. The model combines extreme learning machine with improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and autoregressive integrated moving average (ARIMA). The extreme learning machine model is employed to obtain short-term wind speed predictions, while the autoregressive model is used to determine the best input variables. An ensemble method is used to improve the robustness of the extreme learning machine. To improve the prediction accuracy, the ICEEMDAN-ARIMA method is developed to post process the errors; this method can also be used to preprocess original wind speed. Additionally, this paper reports the results of a comparative study on preprocessing and postprocessing time series data. Three experimental results show that: (1) the error correction is effective in decreasing the prediction error, and the proposed models with error correction are suitable for short-term wind speed forecasting; (2) the ICEEMDAN method is more powerful than other variants of empirical mode decomposition in performing non-stationary decomposition, and the ICEEMDAN-ARIMA method achieves satisfactory performance both for preprocessing and post processing; and (3) for prediction, the preprocessing of time series is more effective than its postprocessing.
关键词	FEATURE-SELECTION DECOMPOSITION ELM EMD OPTIMIZATION ALGORITHM
英文关键词	Extreme learning machine; Empirical mode decomposition; Autoregressive integrated moving average; Wind speed forecasting; Error revision
语种	英语
WOS研究方向	Thermodynamics ; Energy & Fuels ; Mechanics
WOS类目	Thermodynamics ; Energy & Fuels ; Mechanics
WOS记录号	WOS:000430771300021
来源期刊	ENERGY CONVERSION AND MANAGEMENT
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259202
推荐引用方式 GB/T 7714	Wang, Lili,Li, Xin,Bai, Yulong. Short-term wind speed prediction using an extreme learning machine model with error correction[J]. 中国科学院青藏高原研究所,2018,162.
APA	Wang, Lili,Li, Xin,&Bai, Yulong.(2018).Short-term wind speed prediction using an extreme learning machine model with error correction.ENERGY CONVERSION AND MANAGEMENT,162.
MLA	Wang, Lili,et al."Short-term wind speed prediction using an extreme learning machine model with error correction".ENERGY CONVERSION AND MANAGEMENT 162(2018).