Short-Term PV Power Forecasting Using a Hybrid TVF-EMD-ELM Strategy. (13th February 2023)

• Record Type: Journal Article
• Authors: Khelifi, Reski; Guermoui, Mawloud; Rabehi, Abdelaziz; Taallah, Ayoub; Zoukel, Abdelhalim; Ghoneim, Sherif S. M.; Bajaj, Mohit; AboRas, Kareem M.; Zaitsev, Ievgen
• Other Names: Falabretti Davide Academic Editor.
• Is Part Of: International transactions on electrical energy systems. Volume 2023(2023)
• Publication Date: 2023-02-13
• Subjects: Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1155/2023/6413716
• Languages: English
• ISSNs: 2050-7038

Abstract

Abstract : This paper discusses the efficient implementation of a new hybrid approach to forecasting short-term PV power production for four different PV plants in Algeria. The developed model incorporates a time-varying filter-empirical mode decomposition (TVF-EMD) and an extreme learning machine (ELM) as an essence regression. The TVF-EMD technique is used to deal with the fluctuation of PV power data by splitting it into a series of more stable and constant subseries. The specified set of features (intrinsic mode functions (IMFs)) is utilized for training and improving our forecasting extreme learning machine model. The adjusted ELM model is used to evaluate prediction efficiency. The suggested TVF-EMD-ELM model is assessed and verified in various Algerian locations with varying climate conditions. In all examined regions, the TVF-EMD-ELM model generates less than 4% error in terms of normalized root mean square error (nRMSE).