Wind power prediction based on meteorological data visualization

Liu, Shengchi and Xiao, Shuangyue and Liu, Li and Liu, Junqiao (2021) Wind power prediction based on meteorological data visualization. In: GREENETS 2021, 6-7 June 2021, Dalian, People’s Republic of China.

[thumbnail of PDF]
Preview
Text (PDF)
eai.6-6-2021.2307765.pdf - Published Version

Download (944kB) | Preview

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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: decision tree wind power prediction meteorological data visualization correlation analysis
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 10 Sep 2021 13:50
Last Modified: 10 Sep 2021 13:50
URI: https://eprints.eudl.eu/id/eprint/6797

Actions (login required)

View Item
View Item