文章基本信息

标题：Wind Power Prediction Based On Improved Genetic Algorithm and Support Vector Machine
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作者：Li Zhang ; Kui Wang ; Wenli Lin 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：3
页码：1-8
DOI：10.1088/1755-1315/252/3/032052
出版社：IOP Publishing
摘要：With the continuous improvement of wind power generation technology, the installed capacity of wind farms and the scale of wind power interconnection are increasing, which brings huge economic benefits to our country, at the same time, alleviates energy security and brings environmental benefits. However, as an intermittent power supply, the fluctuation and uncertainty of wind power increase the difficulty of security dispatch of power grid and increase the burden of system reserve capacity. Based on this, aiming at single-point wind power prediction and support vector machine method, this paper studies the improved support vector machine wind power prediction based on genetic algorithm. In the prediction model of support vector machine, the improper setting of parameters will lead to the phenomenon of "under-learning" or "over-learning", which directly affects the prediction accuracy. In this paper, genetic algorithm is used to optimize the parameters. The example shows that the GA-SVM model has a better prediction effect than the model with default parameters.