Research on power grid investment capacity prediction model based on multiple method combination

Jie Teng; Yongli Bai; Wei Zhang; Weiyang You

doi:10.1117/12.3024545

13 May 2024 Research on power grid investment capacity prediction model based on multiple method combination

Jie Teng, Yongli Bai, Wei Zhang, Weiyang You

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131598N (2024) https://doi.org/10.1117/12.3024545
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

With the increasingly complex investment environment of China's power grid, accurately predicting the investment capacity of power grid enterprises has become an important prerequisite. Firstly, based on the internal and external environment of the enterprise, factors and indicators that affect the investment capacity of the power grid are selected from three dimensions: internal finance, economy and society, and the power industry. Principal component analysis is conducted to obtain the principal component factors after dimensionality reduction. Then, multiple regression models, backpropagation neural network models, and autoregressive integrated moving average models are used to predict the investment capacity of the power grid. Finally, a combination prediction model is constructed based on the three single prediction models mentioned above, and the weight coefficient of the single prediction model is calculated using nonlinear programming method. The results indicate that compared with a single prediction model, the combined prediction model can achieve higher prediction accuracy in predicting the investment capacity of the power grid, providing a reference for power grid investment decision-making and management.

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

Citation Download Citation

Jie Teng, Yongli Bai, Wei Zhang, and Weiyang You "Research on power grid investment capacity prediction model based on multiple method combination", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131598N (13 May 2024); https://doi.org/10.1117/12.3024545

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