TY - JOUR
T1 - A Novel Cross-Case Electric Vehicle Demand Modeling Based on 3D Convolutional Generative Adversarial Networks
AU - Jahangir, Hamidreza
AU - Sadeghi Gougheri, Saleh
AU - Vatandoust, Behzad
AU - A.Golkar, Mahsa
AU - Aliakbar Golkar, Masoud
AU - Ahmadian, Ali
AU - Hajizadeh, Amin
N1 - Publisher Copyright:
IEEE
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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).
AB - 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).
KW - Artificial Intelligence
KW - Artificial intelligence
KW - Artificial neural networks
KW - Correlation
KW - Deep Learning
KW - Electric Vehicles
KW - Energy Market
KW - Feature extraction
KW - Generative Adversarial Networks
KW - Smart Charging
KW - Solid modeling
KW - Task analysis
KW - Three-dimensional displays
UR - http://www.scopus.com/inward/record.url?scp=85112600134&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2021.3100994
DO - 10.1109/TPWRS.2021.3100994
M3 - Journal article
AN - SCOPUS:85112600134
SN - 0885-8950
VL - 37
SP - 1173
EP - 1183
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 2
ER -