Wind Power Prediction Based On Improved Genetic Algorithm and Support Vector Machine

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Zhang, Li^1 ; Wang, Kui^1 ; Lin, Wenli^1 ; Geng, Tianxiang^2 ; Lei, Zhen^3 ; Wang, Zheng^4

CHSCOM Electric Technology Co. Ltd, Nanjing

210019, China^1

State Grid Ningxia Electric Power Co. Ltd., Yinchuan

750000, China^2

State Grid Jiangsu Electric Power Co. Ltd., Nanjing

210000, China^3

China Electric Power Research Institute Co. Ltd., Beijing

100192, China^4

关键词: Continuous improvements;    Environmental benefits;    Installed capacity;    Power interconnections;    Prediction accuracy;    Setting of parameters;    Support vector machine method;    Wind power predictions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/3/032052/pdf
DOI  :  10.1088/1755-1315/252/3/032052

来源: IOP

【 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.