Faculty Publications

Ev Fleet Charging Load Forecasting Based On Multiple Decomposition With Ceemdan And Swarm Decomposition

Document Type

Article

Keywords

CEEMDAN, electric vehicle, fleet charging, forecasting, signal decomposition, swarm decomposition

Journal/Book/Conference Title

IEEE Access

Volume

10

First Page

62330

Last Page

62340

Abstract

As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R2 value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition.

Department

Department of Applied Engineering and Technical Management

Original Publication Date

1-1-2022

DOI of published version

10.1109/ACCESS.2022.3182499

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