Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting

Wenting Zhao (), Haoran Xu, Peng Chen, Juan Zhang, Jing Li and Tingting Cai
Additional contact information
Wenting Zhao: School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China
Haoran Xu: School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Peng Chen: School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China
Juan Zhang: School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China
Jing Li: School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China
Tingting Cai: School of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030600, China

Energies, 2025, vol. 18, issue 13, 1-25

Abstract: Load forecasting plays a crucial role in power system planning and operational dispatch management. Accurate load prediction is essential for enhancing power system reliability and facilitating the local integration of renewable energy. This paper proposes a hybrid approach combining traditional time series models (ARIMA) with machine learning models (SVR). The particle swarm optimization (PSO) algorithm is improved by adjusting its elastic momentum, and the enhanced APSO algorithm is employed to optimize the adaptive weights of the hybrid model. Consequently, an elastic momentum-enhanced adaptive weighted load forecasting model (APSO-ARIMA-SVR) is developed. Numerical simulations using real-world datasets validate the model’s effectiveness. Results demonstrate that the proposed APSO-ARIMA-SVR model achieves optimal fitting performance, with prediction errors of 274.23 (MAE) and 321.50 (RMSE), representing the lowest errors among all comparative models.

Keywords: hybrid forecasting model; elastic momentum; adaptive weights; short-term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/13/3263/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/13/3263/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:13:p:3263-:d:1684677

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().