Plug-in electric vehicles demand modeling in smart grids: A deep learning-based approach: Wip abstract

Hamidreza Jahangir, Charalambos Konstantinou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


In smart grids, Plug-in Electric Vehicles (PEVs) are considered components of the power demand. PEVs have highly stochastic behavior, and to manage this stochastic load efficiently, intermediary bodies, widely known as aggregators, have been developed in the literature. In order to handle the PEVs charging demand from both technical and financial points of view, aggregators include tools based on Internet-of-Things (IoT) technology, which can observe the users' historical behavior and estimate their travel behavior and the requested charging demand. In the near future, the increase in the share of PEV adoption will transform the PEVs demand modeling framework into a "big-data"Cyber-Physical System (CPS). We present a novel artificial intelligence approach based on the deep learning concept to tackle this large dimension problem. To investigate users' different behavior, the PEVs are classified into different groups based on their driving patterns. Then, each class is assigned to its respective deep convolutional neural networks. The proposed method's performance will be investigated in the day-ahead energy market.
Original languageEnglish (US)
Title of host publicationICCPS 2021 - Proceedings of the 2021 ACM/IEEE 12th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2021)
PublisherAssociation for Computing Machinery, Inc
Number of pages2
ISBN (Print)9781450383530
StatePublished - May 19 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13


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