Ultra-short-term wind power forecasting model based on improved whale algorithm and LSTM neural network

Wenhao Li; Shi Qiao; Haoqiang Zhang; Qixuan Zheng; Shu Yang

doi:10.1117/12.2640185

1 August 2022 Ultra-short-term wind power forecasting model based on improved whale algorithm and LSTM neural network

Wenhao Li, Shi Qiao, Haoqiang Zhang, Qixuan Zheng, Shu Yang

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122570N (2022) https://doi.org/10.1117/12.2640185
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

The paper discusses an ultra-short-term wind power prediction model that optimizes the parameters of the LSTM model by improving the whale algorithm. After the initial wind power-related data is pre-processed and normalized, the training model is constructed by using the long-term short-term neural network, LSTM neural network, whale algorithm optimization, ultra-short-term wind power account for optimization LSTM. The results show that the difference is positive, so its correctness, effectiveness, and optimization parameters can be considered.

Citation Download Citation

Wenhao Li, Shi Qiao, Haoqiang Zhang, Qixuan Zheng, and Shu Yang "Ultra-short-term wind power forecasting model based on improved whale algorithm and LSTM neural network", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122570N (1 August 2022); https://doi.org/10.1117/12.2640185

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