Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm
Open Access
- 3 April 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 68874-68882
Abstract
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. 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. 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.Keywords
Funding Information
- National Natural Science Foundation of China (61976175, 51507140)
- Key project of Natural Science Basic Research plan in Shaanxi Province of China (2019JZ-05)
- China Scholarship Council (CSC) State Scholarship Fund International Clean Energy Talent Project ([2018]5046,[2019]157)
- Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education (2018AC02)
- State Key Laboratory of Electrical Insulation and Power Equipment
- Natural Science Basic Research Plan of Shaanxi Province (2018JM5041)
- Science and Technology Plan Project of Xi’an (GXYD14.23, GXYD14.10)
- Xi’an Beilin District Science and Technology Project
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