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Special Issue "Future Electricity Network Infrastructures"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: 10 November 2022.

Special Issue Editors

Prof. Dr. Tek-Tjing Lie
E-Mail Website
Guest Editor
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: AI applications to power systems; power system control and operation; Smart grids; renewable energy resources; energy management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guojie Li
E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Interests: renewable energy integrated power system operation and control; renewable energy control

Special Issue Information

Dear Colleagues,

The 7th Asia Conference on Power and Electrical Engineering (ACPEE 2022) will be held in Hangzhou, China, April 15–17, 2022. This conference aims to provide a platform for researchers and practicing engineers to share their ideas, recent developments, and successful practices in power and electrical engineering.  The conference will publish high-quality papers that are strictly related to the various theories and practical applications in the area of machine learning applications based on future power system operations with high penetration of renewable resources and its related network architecture. The conference will be a forum for excellent discussions that will put forward new ideas and promote collaborative research. We are sure that the proceedings will serve as an important research source of references and knowledge, which will lead not only to scientific and engineering progress but also other new products and processes. The conference papers falling in the scope of Sensors at this conference are invited to submit the extended versions to this Special Issue on Future Electricity Network Infrastructures for publication.

Prof. Dr. Tek-Tjing Lie
Prof. Dr. Guojie Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • future electricity network infrastructures
  • machine learning
  • DC network architecture
  • smart and intelligent buildings
  • smart EV charging
  • smart cities

Published Papers (1 paper)

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Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply
Sensors 2021, 21(21), 7191; - 29 Oct 2021
Cited by 2 | Viewed by 532
Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. [...] Read more.
Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches. Full article
(This article belongs to the Special Issue Future Electricity Network Infrastructures)
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