Studies from Xi’an University of Technology Yield New Data on Engineering (Wind Power Prediction of Kernel Extreme Learning Machine Based On Differential Evolution Algorithm and Cross Validation Algorithm)
- Aug 11, 2020 10:19 pm GMTAug 12, 2020 12:34 am GMT
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2020 AUG 11 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- A new study on Engineering is now available. According to news reporting from Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “As fossil fuel is being depleted, the percentage of wind power capacity in total electricity generation is increasing. In order to improve the absorption capacity of wind power, wind power prediction has been introduced.”
Funders for this research include National Natural Science Foundation of China, Key Project of Natural Science Basic Research Plan in Shaanxi Province of China, China Scholarship Council (CSC) State Scholarship Fund International Clean Energy Talent Project, Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, State Key Laboratory of Electrical Insulation and Power Equipment, Natural Science Basic Research Plan of Shaanxi province, Science and Technology Plan Project of Xi’an, Xi’an Beilin District Science and Technology Project.
The news correspondents obtained a quote from the research from the Xi’an University of Technology, “Aiming at the disadvantage of low prediction accuracy and unstable model of traditional extreme learning machine (ELM), a kernel extreme learning machine based on differential evolution (DE) and cross validation optimization method is proposed to predict short-term wind power generation. Firstly, the average mean square error (MSE) verified by k folding and cross validation is adopted as the error function of the model to improve the stability and generalization performance of the model.”
According to the news reporters, the research concluded: “Secondly, differential evolution algorithm is used to optimize the regularization coefficient and kernel width of the kernel extreme learning machine with cross validation and improve the precision of model is 8.34% Finally, compared with the application of extreme learning machine with genetic algorithm and cross validation to a wind farm prediction case in northwest China, the experimental results show that the convergence rate of this method is twice that of genetic algorithm (GA) optimization algorithm, and the accuracy is higher.”
For more information on this research see: Wind Power Prediction of Kernel Extreme Learning Machine Based On Differential Evolution Algorithm and Cross Validation Algorithm. IEEE Access, 2020;8():68874-68882. IEEE Access can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.
Our news journalists report that additional information may be obtained by contacting Ning Li, Xi’an University of Technology, School of Electrical Engineering, Xian 710048, People’s Republic of China. Additional authors for this research include Fuxing He, Wentao Ma, Ruotong Wang and Xiaoping Zhang.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/ACCESS.2020.2985381. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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