On the V2G capacity of shared electric vehicles and its forecasting through MAML‐CNN‐LSTM‐Attention algorithm

Author:

Xu Mingkuo1ORCID,Ren Hui1,Chen Ping12,Xin Guoyu13

Affiliation:

1. North China Electric Power University Baoding China

2. State Grid Hebei Marketing Service Center Shijiazhuang China

3. State Grid Hebei Electric Power Co, Ltd Power Supply Company Hengshui China

Abstract

AbstractShared electric vehicles (shared EVs) could be treated as energy storage and participate in electricity market services. In that case, the schedulable capacity that shared EV provide to grid in future time needs to be predicted accurately. Based on open data provided by a shared vehicle rental project, this research firstly proposes a method to build up schedulable capacity data set to prepare sample data for the deep learning‐based forecasting task. Secondly, a schedulable capacity forecasting model by MAML‐CNN‐LSTM‐Attention is proposed. Through the model, ultra‐short prediction (1 h) of aggregated schedulable vehicle‐to‐grid capacity of shared EVs is performed. The proposed model uses model‐agnostic meta‐learning (MAML) to optimize initial parameters of network to quickly adapt to changes in characteristics caused by different travel habits of different functional communities; uses two‐layer convolutional neural network (CNN) with long short‐term memory neural network (LSTM) and Attention mechanism to extract temporal‐spatial features of schedulable capacity for important historical moments. Simulation results show that the proposed model has a mean absolute percentage error (MAPE) of 1.13% and goodness of fit of 0.976, the MAML decreases the MAPE by 1.04%, its stability and accuracy were better than previous used models. The research can act as useful decision aids for shared EV operators to participate in market services.

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications;Journal of Sensor and Actuator Networks;2024-08-27

2. Deep Learning for Enterprise Sales Prediction: Haressing CNN-LSTM-Attention Model;2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA);2024-02-27

3. Optimal operation of microgrid with consideration of V2G's uncertainty;IET Generation, Transmission & Distribution;2023-10-30

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