Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
IEEE Transactions on Industrial Informatics
Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.
Luo, Xiong; Sun, Jiankun; Wang, Long; Wang, Weiping; Zhao, Wenbing; Wu, Jinsong; Wang, Jenq-Haur; and Zhang, Zijun, "Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy" (2018). Electrical Engineering & Computer Science Faculty Publications. 447.
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This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB0702300, in part by the National Natural Science Foundation of China under Grant 61174103 and Grant 61603032, in part by the University of Science and Technology Beijing—National Taipei Universityof Technology Joint Research Program under Grant TW201705, in part by the Fundamental Research Funds for the Central Universities under Grant 06500025 and Grant 06500078, in part by the Foundation from theNational Taipei University of Technology of Taiwan under Grant NTUT-USTB-106-5, and in part by the Chile CONICYT FONDECYT (Regular)Project under Grant 1181809.