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A new hybrid model combining EMD and neural network for multi-step ahead load forecasting.

Authors :
Malik, Hasmat
Alotaibi, Majed A.
Almutairi, Abdulaziz
Chaudhary, Gopal
Srivastava, Smriti
Source :
Journal of Intelligent & Fuzzy Systems. 2022, Vol. 42 Issue 2, p1099-1114. 16p.
Publication Year :
2022

Abstract

The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10641246
Volume :
42
Issue :
2
Database :
Academic Search Index
Journal :
Journal of Intelligent & Fuzzy Systems
Publication Type :
Academic Journal
Accession number :
156139200
Full Text :
https://doi.org/10.3233/JIFS-189775