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A Systematic Review and Proposed Hybrid SSA-CEEMDAN-Attention Framework for Stochastic Electric Vehicle Charging Demand Forecasting
Subject area: Science,Engineering and Technology · Area of research: Computer Science
DOI: https://doi.org/10.64388/IREV9I11-1718001
Abstract
The exponential surge in Electric Vehicle (EV) adoption presents a high-dimensional, spatio-temporal stochastic optimization problem that threatens the operational stability of contemporary power distribution networks. Precise short-term demand forecasting is a mechanical necessity for integrating charging stations (EVCS) with volatile renewable energy sources. Existing literature frequently fails to synchronize the “Double Uncertainty” inherent in coupled behavioral and meteorological patterns, while simultaneously exceeding the computational thresholds of decentralized edge devices. This study synthesizes 25 recent works published between 2019 and 2026 and proposes a novel hybrid SSA-CEEMDAN-Attention-BiLSTM architecture. Preliminary benchmarking indicates that the proposed selfoptimizing denoising framework achieves a 15% reduction in Mean Absolute Percentage Error (MAPE) under high-volatility conditions, providing a computationally efficient blueprint for the 2030 smart grid transition.
Keywords
Electric Vehicle (EV), Load Forecasting, CEEMDAN, Attention Mechanism, Smart Grid, Hybrid Models, Deep Learning, Optimization
How to cite this paper
@article{1718001,
author = {Ravi Kishan Varma, Dr. S. Balamurugan},
title = {A Systematic Review and Proposed Hybrid SSA-CEEMDAN-Attention Framework for Stochastic Electric Vehicle Charging Demand Forecasting},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2959-2962},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718001.pdf},
abstract = {The exponential surge in Electric Vehicle (EV) adoption presents a high-dimensional, spatio-temporal stochastic optimization problem that threatens the operational stability of contemporary power distribution networks. Precise short-term demand forecasting is a mechanical necessity for integrating charging stations (EVCS) with volatile renewable energy sources. Existing literature frequently fails to synchronize the “Double Uncertainty” inherent in coupled behavioral and meteorological patterns, while simultaneously exceeding the computational thresholds of decentralized edge devices. This study synthesizes 25 recent works published between 2019 and 2026 and proposes a novel hybrid SSA-CEEMDAN-Attention-BiLSTM architecture. Preliminary benchmarking indicates that the proposed selfoptimizing denoising framework achieves a 15% reduction in Mean Absolute Percentage Error (MAPE) under high-volatility conditions, providing a computationally efficient blueprint for the 2030 smart grid transition.},
keywords = {Electric Vehicle (EV), Load Forecasting, CEEMDAN, Attention Mechanism, Smart Grid, Hybrid Models, Deep Learning, Optimization},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1718001}
}