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\begin{document}

\title{On the V2G Capacity of Shared Electric Vehicles and its Forecasting
through MAML-CNN-LSTM-Attention Algorithm}



\author[1]{Mingkuo Xu}%
\author[1]{Hui Ren}%
\author[2]{Ping Chen}%
\author[2]{Guoyu Xin}%
\affil[1]{North China Electric Power University - Baoding Campus}%
\affil[2]{State Grid Hebei Electric Power Company}%


\vspace{-1em}



  
  \date{June 07, 2024}


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\begin{abstract}
When acting as the shared energy storage and participating in
electricity market services, the schedulable capacity that shared
electric vehicle (shared EV) will provide to the grid needs in future
time needs to be predicted accurately. This research first proposes a
method to construct a schedulable capacity dataset for shared EVs based
on publicly available shared vehicle rental service data. Secondly, a
schedulable capacity evaluation model based on model-agnostic
meta-learning, convolutional neural network, long short-term neural
network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed.
Through the model, the aggregated schedulable capacity of shared EVs in
different functional communities for the coming 60 minutes is predicted.
Model uses MAML to fine-tune the meta-prediction network through
multi-task training to quickly adapt to feature changes caused by
different travel habits of different functional communities; CNN-LSTM is
used to learn spatial features of schedulable capacity and efficiently
extract high-dimensional temporal features from historical sequences;
Attention mechanism is used to further improve model prediction
accuracy. Simulations show that the model proposed in this paper
outperforms other existing models and can reliably predict the
schedulable capacity for different date types and functional areas,
providing useful decision aids for shared EV operators to participate in
market services.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Word version of the above document.rar`  available at \url{https://authorea.com/users/596042/articles/629723-on-the-v2g-capacity-of-shared-electric-vehicles-and-its-forecasting-through-maml-cnn-lstm-attention-algorithm}

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