Advances in Machine Learning, IoT and Big Data for Sustainable Communities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 14963

Special Issue Editors


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Guest Editor
Department of Economic Informatics and Cybernetics, University of Economic Studies, 010374 Bucharest, Romania
Interests: IoT; database systems; big data; deep learning; natural language processing; energy management systems; digital twins
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
Interests: multi-vector energy systems; smart buildings; local flexibility markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is to address, by using recent advances in artificial intelligence, machine learning and big data, the challenges of sustainable communities that generate large volumes of data from sensors, smart meters, IoT devices and smartphones. Sustainable communities imply a consistent use of technology for the benefit of citizens: smart grids, smart water management facilities, smart waste management systems, smart traffic and transportation systems, smart security systems or e-governance structures in such communities. These components ingest and generate a multitude of structured, semi-structured or unstructured data that require processing in real time or retrospect. Based on these big data, machine learning algorithms can be developed to aid in preventing problems (e.g., frauds, malfunctions, space scarcity, supply shortages) or identifying patterns and trends (e.g., analyzing electricity consumption, forecasting growth areas or equipment status). Numerous research opportunities can be explored for the safe operation of these components, flexibly running algorithms and avoiding big data bias, off-the-shelf machine learning algorithms and big data hubris.

Topics:

  • Machine learning theory and applications;
  • Big data management, processing and analytics;
  • Smart cities and sustainable communities;
  • IoT management and integration with utilities;
  • Cloud, distributed and parallel computing;
  • IoT, mobile-embedded and multimedia solutions.

New papers, or extended versions of papers presented at the 21st International Conference on Informatics on Economy (IE2022), are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of the Electronics journal, and accepted based on quality, originality, novelty, and relevance to the theme of the Special Issue.

Dr. Simona-Vasilica Oprea
Dr. Fabrizio Marozzo
Dr. Xiaolong Jin
Guest Editors

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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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • machine learning theory and applications
  • big data management, processing and analytics
  • smart cities and sustainable communities
  • IoT management and integration with utilities
  • cloud, distributed and parallel computing
  • IoT, mobile-embedded and multimedia solutions

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Published Papers (4 papers)

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Research

17 pages, 11683 KiB  
Article
Visual Extraction of Refined Operation Mode of New Power System Based on IPSO-Kmeans
by Xiaoli Guo, Qingyu Shan, Zhenming Zhang and Zhaoyang Qu
Electronics 2023, 12(10), 2326; https://doi.org/10.3390/electronics12102326 - 22 May 2023
Cited by 3 | Viewed by 1529
Abstract
Due to the influence of the high proportion of renewable energy penetration, the time-varying and complex operation mode of the new power system is gradually increasing, leading to a lack of fineness and practicality of traditional operation modes. To this end, a new [...] Read more.
Due to the influence of the high proportion of renewable energy penetration, the time-varying and complex operation mode of the new power system is gradually increasing, leading to a lack of fineness and practicality of traditional operation modes. To this end, a new visual extraction method for fine operation mode of power system is proposed. Specifically, aiming at the dimensional problem between high-dimensional electrical characteristic variables, a power grid operation data preprocessing method based on maximum absolute standardization (MaxAbs) is designed. Then, in order to reduce the impact of redundant features on the accuracy of the operation mode extraction results, the Pearson correlation coefficient is introduced to optimize the feature space relationship matrix, constructing a screening model of operating mode characteristic variables based on pearson kernel principal component analysis (P_KPCA). Then, with the clustering elbow index as the constraint condition, a K-means algorithm based on improved particle swarm optimization (IPSO-Kmeans) was proposed to realize fine operation mode extraction. Finally, the experimental analysis is carried out with the actual operation data of the power grid for one year and based on uniform manifold approximation and projection (UMAP) to visualize the extraction results of the operation mode. The validity and accuracy of the proposed method are verified. Full article
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27 pages, 7498 KiB  
Article
Building Integrated Photovoltaics 4.0: Digitization of the Photovoltaic Integration in Buildings for a Resilient Infra at Large Scale
by Digvijay Singh, Shaik Vaseem Akram, Rajesh Singh, Anita Gehlot, Dharam Buddhi, Neeraj Priyadarshi, Gulshan Sharma and Pitshou N. Bokoro
Electronics 2022, 11(17), 2700; https://doi.org/10.3390/electronics11172700 - 29 Aug 2022
Cited by 22 | Viewed by 4608
Abstract
Building integrated photovoltaic (BIPV) systems have gained a lot of attention in recent years as they support the United Nations’ sustainable development goals of renewable energy generation and construction of resilient infrastructure. To make the BIPV system infra resilient, there is a need [...] Read more.
Building integrated photovoltaic (BIPV) systems have gained a lot of attention in recent years as they support the United Nations’ sustainable development goals of renewable energy generation and construction of resilient infrastructure. To make the BIPV system infra resilient, there is a need to adopt digital technologies such as the internet of things (IoT), artificial intelligence (AI), edge computing, unmanned aerial vehicles (UAV), and robotics. In this study, the current challenges in the BIPV system, such as the rise in the temperature of the PV modules, the occurrence of various faults, and the accumulation of dust particles over the module surface, have been identified and discussed based on the previous literature. To overcome the challenges, the significance and application of the integration of these digital technologies in the BIPV system are discussed along with the proposed architecture. Finally, the study discusses the vital recommendations for future directions, such as ML and DL for image enhancement and flaws detection in real-time image data; edge computing to implement DL for intelligent BIPV data analytics; fog computing for 6G assisted IoT network in BIPV; edge computing integration in UAV for intelligent automation and detection; augmented reality, virtual reality, and digital twins for virtual BIPV systems with research challenges of real-time implementation in the BIPV. Full article
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25 pages, 12113 KiB  
Article
Secure and Anonymous Voting D-App with IoT Embedded Device Using Blockchain Technology
by Cristian Toma, Marius Popa, Catalin Boja, Cristian Ciurea and Mihai Doinea
Electronics 2022, 11(12), 1895; https://doi.org/10.3390/electronics11121895 - 16 Jun 2022
Cited by 13 | Viewed by 4305
Abstract
The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures [...] Read more.
The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures complete voter anonymity and end-to-end security for all entities participating in the electronic voting process. The paper outlines the solution’s two layers. Implementation details are presented for the e-voting application, which was deployed inside of an IoT embedded device. The solution and presented protocols provide two major properties: privacy and verifiability. To ensure privacy, the proposed solution protects the secrecy of each electronic vote. As for implementing verifiability, the solution prevents a corrupt authority from faking in any way the process of counting the votes. Both properties are achieved in the presented solution e-VoteD-App. Full article
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17 pages, 6358 KiB  
Article
Smart Cities and Awareness of Sustainable Communities Related to Demand Response Programs: Data Processing with First-Order and Hierarchical Confirmatory Factor Analyses
by Simona-Vasilica Oprea, Adela Bâra, Cristian-Eugen Ciurea and Laura Florentina Stoica
Electronics 2022, 11(7), 1157; https://doi.org/10.3390/electronics11071157 - 6 Apr 2022
Cited by 5 | Viewed by 2875
Abstract
The mentality of electricity consumers is one of the most important entities that must be addressed when dealing with issues in the operation of power systems. Consumers are used to being completely passive, but recently these things have changed as significant progress of [...] Read more.
The mentality of electricity consumers is one of the most important entities that must be addressed when dealing with issues in the operation of power systems. Consumers are used to being completely passive, but recently these things have changed as significant progress of Information and Communication Technologies (ICT) and Internet of Things (IoT) has gained momentum. In this paper, we propose a statistical measurement model using a covariance structure, specifically a first-order confirmatory factor analysis (CFA) using SAS CALIS procedure to identify the factors that could contribute to the change of attitude within energy communities. Furthermore, this research identifies latent constructs and indicates which observed variables load on or measure them. For the simulation, two complex data sets of questionnaires created by the Irish Commission for Energy Regulation (CER) were analyzed, demonstrating the influence of some exogenous variables on the items of the questionnaires. The results revealed that there is a relevant relationship between the social–economic and the behavioral factors and the observed variables. Furthermore, the models provided a good fit to the data, as measured by the performance indicators. Full article
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