학술논문
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Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Three-dimensional displays
Artificial neural networks
Correlation
Feature extraction
Artificial intelligence
Task analysis
Solid modeling
deep learning
electric vehicles
generative adversarial networks
energy market
smart charging
A Novel Cross-Case Electric Vehicle Demand Modeling Based on 3D Convolutional Generative Adversarial Networks
Document Type
Periodical
Author
Source
IEEE Transactions on Power Systems IEEE Trans. Power Syst. Power Systems, IEEE Transactions on. 37(2):1173-1183 Mar, 2022
Subject
Language
ISSN
0885-8950
1558-0679
1558-0679
Abstract
Electric Vehicle (EV) demand modeling constitutes the cornerstone of studies aiming to facilitate the integration of EVs into the power system. The different characteristics of the EV demand (departure time, arrival time, and electric demand), as well as the correlation between thereof, render EV demand modeling a complex task. The Majority of previous methods, which were developed based on the Monte Carlo simulation, are unable to observe and preserve the correlation between EV demand characteristics; because, in these methods, the EV demand characteristics are generated separately in an unsupervised manner. This study proposes a novel semi-supervised EV demand modeling approach by mapping the different EV demand characteristics into a three-dimensional (3D) space as a 3D image. To effectively realize the 3D EV demand modeling, we have employed Generative Adversarial Networks (GANs) with a 3D convolutional structure to develop EV-GANS network—a GANs structure tailored to the needs of EV demand modeling in environments hosting high demand diversity such as EV charging stations. Numerical results confirmed the effectiveness of the proposed EV-GANS in estimating the trend of the actual EV demand on the test day with a small error margin compared to the existing benchmark generation-based methods (Monte Carlo and Copula).