Utilizing nonlinear models in power systems for short-term load forecasting

Xi Huang; Qiu Hong; Fei Chen; Haichuan Zhang; Wentao Huang

doi:10.1117/12.3052452

30 December 2024 Utilizing nonlinear models in power systems for short-term load forecasting

Xi Huang, Qiu Hong, Fei Chen, Haichuan Zhang, Wentao Huang

Author Affiliations +

Proceedings Volume 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024); 133941V (2024) https://doi.org/10.1117/12.3052452
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 2024, Hohhot, China

Abstract

This paper investigates the application of nonlinear forecasting models in Short-term Load Forecasting (STLF). Given the limitations of traditional linear models in dealing with complex load data, nonlinear forecasting models are invoked to enhance forecasting accuracy. Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) are constructed and validated, and the initial use of ensemble learning idea to integrate single nonlinear models is proposed. The empirical analyses showed that the nonlinear models outperform the linear ones in terms of prediction accuracy and stability. The results demonstrated that MLP-SDLW models with 10 variables performed best with Mean Average Percentage Error (MAPE) 5.047%. Meanwhile the ensemble models reflected the advantage of higher accuracy in observation.

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

Citation Download Citation

Xi Huang, Qiu Hong, Fei Chen, Haichuan Zhang, and Wentao Huang "Utilizing nonlinear models in power systems for short-term load forecasting", Proc. SPIE 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 133941V (30 December 2024); https://doi.org/10.1117/12.3052452

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