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Smart Cities
Special Issues
Smart Electronics, Energy, and IoT Infrastructures for Smart Cities
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Smart Electronics, Energy, and IoT Infrastructures for Smart Cities

A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: 1 August 2024 | Viewed by 1137

Special Issue Editors

Dr. Zheng Xu

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Guest Editor
School of Computer Science, Shanghai University, Shanghai 201142, China
Interests: IoT; cloud computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jemal Abawajy

E-Mail Website
Guest Editor
Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC 3125, Australia
Interests: cyber security; blockchain; internet of things; cloud computing; artificial intelligence applications in business
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of sophisticated artificial intelligence technology into smart energy systems and grids, will require a multi-fold understanding of computational, economic and social issues. This kind of socio-technical platform and integration needs an initial definition of the domain and a well-grounded specification of the research problem. The quest for sustainable development has involved society, academia and industry in finding tangible solutions for the development of the world beyond the post-industrial society and its implications. Consequently, not only the new processes associated with the changes, but also the literature describing the resulted challenges have been enriched. The evolution of smart cities research integrates multidisciplinary contributions. Sophisticated disruptive technologies set new challenges for the investigation of sustainable models of economic development. The research issue of energy management is amongst of the core application areas for both smart cities research and disruptive technologies adoption. In our work, this investigation is a key research objective.

The artificial intelligence domain, with advanced machine learning and cognitive computing capabilities, seems to be a key to enabling unforeseen efficiency capabilities in the context of smart energy grids. This Special Issue intends to contribute with a novel approach for the development of a theoretical framework for a scientific debate on how AI can enhance the efficiency of the RE sector towards economic efficiency and sustainability. In the context of this Special Issue, which is related to future smart cities research, we contribute a new significant item in the research agenda: AI-driven RE services for maximum social impact and economic efficiency. The main contribution of this special issue will be the integration of social sciences research with advanced information systems research.

Subject of interests include, but are not limited to:

  • smart match of supply with demand for smart cities;
  • intelligent storage for smart cities;
  • centralized control system for smart cities;
  • smart microgrids for smart cities;
  • grid stability and reliability, safety operations;
  • accurate demand forecast and weather forecast;
  • efficient demand-side management;
  • energy storage operations;
  • market design and operations.

You may choose our Joint Special Issue in Electronics.

Dr. Zheng Xu
Prof. Dr. Jemal Abawajy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 submissions that pass pre-check are 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. Smart Cities 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 2000 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.

Keywords

  • electronics
  • energy
  • IoT infrastructures
  • smart cities

Published Papers (1 paper)

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Research

28 pages, 11761 KiB  
Article
Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring
by Usamah Rashid Qureshi, Aiman Rashid, Nicola Altini, Vitoantonio Bevilacqua and Massimo La Scala
Smart Cities 2024, 7(3), 1261-1288; https://doi.org/10.3390/smartcities7030053 - 28 May 2024
Viewed by 240
Abstract
Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study [...] Read more.
Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering. Full article
(This article belongs to the Special Issue Smart Electronics, Energy, and IoT Infrastructures for Smart Cities)
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