Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, People’s Republic of China

Research Article

Wind power prediction based on meteorological data visualization

Download80 downloads
  • @INPROCEEDINGS{10.4108/eai.6-6-2021.2307765,
        author={Shengchi  Liu and Shuangyue  Xiao and Li  Liu and Junqiao  Liu},
        title={Wind power prediction based on meteorological data visualization},
        proceedings={Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, People’s Republic of China},
        publisher={EAI},
        proceedings_a={GREENETS},
        year={2021},
        month={8},
        keywords={decision tree wind power prediction meteorological data visualization correlation analysis},
        doi={10.4108/eai.6-6-2021.2307765}
    }
    
  • Shengchi Liu
    Shuangyue Xiao
    Li Liu
    Junqiao Liu
    Year: 2021
    Wind power prediction based on meteorological data visualization
    GREENETS
    EAI
    DOI: 10.4108/eai.6-6-2021.2307765
Shengchi Liu1, Shuangyue Xiao1, Li Liu1, Junqiao Liu1,*
  • 1: Department of Information Science and Engineering Dalian Polytechnic University Dalian, P. R. China
*Contact email: dalianliujunqiao@126.com

Abstract

With the development of clean energy, wind power generation has become one of the most important power generation methods. However, the output power of wind power generation system is characterized by uncertainty, so the effective interval prediction of wind power is an effective method to reduce the uncertainty.In this article, through multi-channel multi-dimensional meteorological data, visual correlation analysis, and in-depth analysis of the main factors affecting wind power, put forward based on the extreme gradient promotion (XGB) improved LGB model to forecast. In addition, in order to improve the model calculating speed and accuracy, using principal component analysis was carried out on the original data dimension reduction analysis and visualization processing, then predicted the results compared with the actual situation, to verify the validity of the established model, it shows that this method can be applied to the era of big data of wind power prediction in the future.