EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition.

Dokur, Emrah; Erdogan, Nuh; Kucuksari, Sadik

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.

Citation

DOKUR, E., ERDOGAN, N. and KUCUKSARI, S. 2022. EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition. IEEE access [online], 10, pages 62330-62340. Available from: https://doi.org/10.1109/ACCESS.2022.3182499

• Journal Article Type: Article
• Acceptance Date: Jun 7, 2022
• Online Publication Date: Jun 13, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Jun 16, 2022
• Publicly Available Date: Jun 16, 2022
• Journal: IEEE access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Pages: 62330-62340
• DOI: https://doi.org/10.1109/ACCESS.2022.3182499
• Keywords: Electric vehicle charging; Power consumption prediction; Power grid usage models; Signal decomposition; Swarm decomposition; CEEMDAN; Electric vehicle; Fleet charging; Forecasting
• Public URL: https://rgu-repository.worktribe.com/output/1688232