Research on wind power prediction based on machine learning

Long Gao; Denglong Lu; Lifeng Fan; Jinghui Zhang; Yahua Wang

doi:10.1117/12.3024304

13 May 2024 Research on wind power prediction based on machine learning

Long Gao, Denglong Lu, Lifeng Fan, Jinghui Zhang, Yahua Wang

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131590B (2024) https://doi.org/10.1117/12.3024304
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

The accuracy of wind and solar power prediction not only affects the assessment of "two regulations" for various wind and solar power plants, but also affects the power generation revenue of wind and solar power plants in the electricity market. Regarding the power prediction of regional wind and photovoltaic power plant clusters, the traditional method of summing up the power of individual stations has low accuracy and efficiency due to different station construction times and uneven single station forecasting accuracy. Therefore, a machine learning method is used to reasonably divide the wind power plants and photovoltaic power station clusters in the region, select representative power stations, and combine historical power data to optimize and correct the numerical prediction elements. Then, a short-term power prediction framework model based on the wind power plants and photovoltaic power station clusters is established using the BP neural network method to improve the prediction accuracy and efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Long Gao, Denglong Lu, Lifeng Fan, Jinghui Zhang, and Yahua Wang "Research on wind power prediction based on machine learning", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131590B (13 May 2024); https://doi.org/10.1117/12.3024304

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available