Deep learning-based energy optimization for electric vehicles integrated smart micro grid

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributed


Applying renewable energy in a smart micro grid (MG) is increasingly receiving attention to reduce greenhouse gas emissions. However, the mismatch between supply and demand hinders the realization of this process. With the widespread use of plug-in electric vehicles (EVs) and the development of emerging mobile edge cloud (MEC), intelligent energy optimization becomes a way to address the challenge. Therefore, in this paper, we propose a novel two-stage approach based on deep learning (DL) to reduce overall energy cost for sharing EVs integrated MG by forecasting its state and optimizing EVs scheduling. Our simulation results show that the joint design of forecasting and optimization reduces the overall energy consumption and the payment to the external grid.


Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
Number of pages7
ISBN (electronic)978-1-5386-8347-7
Publication statusPublished - 17 May 2022

External IDs

Scopus 85136383746
Mendeley 8eaf2939-601f-3fbc-ba5d-2dc739d5584c
dblp conf/icc/ZhangWBBF22
unpaywall 10.1109/icc45855.2022.9838771


Sustainable Development Goals


  • deep learning, electric vehicles, micro grid, mobile edge cloud, renewable energy