Comparative U Of Machine Learning Model to Explore the Impact of Geographical Factors on Wind Speed

Zihe Zhao

doi:10.54097/n0ed5d04

Authors

Zihe Zhao Brunel Lodon School, North China University of Technology, Beijing 100144, China

DOI:

https://doi.org/10.54097/n0ed5d04

Keywords:

Location of wind turbines; XGBoost model; Maximize energy efficiency.

Abstract

At present, society's awareness of environmental protection has been enhanced, and the demand for simultaneous development and protection of environmental purification and sustainability is increasing. As a clean energy source, wind power generation can well replace a part of fossil energy and prevent the inevitable pollutants caused by the use of fossil energy. However, the generators that wind power depends on do not have the ability to migrate, so the location of wind turbines is particularly important. It can not only occupy the potential residence of human beings, but also provide a large and stable amount of power generation. In this paper, three different machine learning models are used to explore the impact of a number of geographical factors on wind speed and to predict the future trend of wind speed. By comparing the three different models, the most suitable model with the highest fitting rate is XGBoost model, and its corresponding is 0.92. The scatter diagram of wind speed prediction and longitude and latitude produced in this paper can further assist in the location of wind turbines to maximize energy efficiency.

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