Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Yanmei Huang, Najmul Hasan, Changrui Deng and Yukun Bao

Energy, 2022, vol. 239, issue PC

Abstract: Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also has a great interest to investors and energy policy maker as well as government. Literature reveals that 1% error drop of forecast can reduce 10 million pounds operational cost. Thus, this study proposed a novel hybrid predictive model built upon multivariate empirical mode decomposition (MEMD) and support vector regression (SVR) with parameters optimized by particle swarm optimization (PSO), which is able to capture precise electricity peak load. The novelty of this study mainly comes from the application of MEMD, which enables the multivariate data decomposition to effectively extract inherent information among relevant variables at different time frequency during the deterioration of multivariate over time. Two real-world load data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia have been considered to verify the superiority of the proposed MEMD-PSO-SVR hybrid model. The quantitative and comprehensive assessments are performed, and the results indicate that the proposed MEMD-PSO-SVR method is a promising alternative for day-ahead electricity peak load forecasting.

Keywords: Day-ahead peak load forecasting; Multivariate empirical mode decomposition (MEMD); Particle swarm optimization (PSO); Hybrid model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221024932
Full text for ScienceDirect subscribers only

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:eee:energy:v:239:y:2022:i:pc:s0360544221024932

DOI: 10.1016/j.energy.2021.122245

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().