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Smart Embedded Technologies and Sensors for Sustainable Renewable Energy and System Applications

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 3028

Special Issue Editors


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Guest Editor
Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, 500036 Brasov, Romania
Interests: renewable energy systems; artificial intelligence; machine learning; deep learning; optimization; soft computing; modeling and simulation; computer vision and pattern recognition; IoT and embedded systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaïb Doukkali University of El Jadida, El Jadida M-24000, Morocco
Interests: performance analysis; monitoring; lifetime analysis; fault detection; control management; power electronics; hybrid renewable energy; mathematical modelling; optimization and meta-heuristic algorithm; computational intelligence; photovoltaic and power energy; forecasting; fuel cell; radar; radio frequency; electromagnetic and electronic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable renewable energy systems have recently been widely implemented in various locations, and the necessity to monitor, regulate, and manage the functioning of the injected power provided by renewable energy sources has drawn the attention of many academics. Through utilizing machine learning and artificial intelligence algorithms, smart embedded systems and digital sensors play an important role in the Internet of Things and Industry 5.0. IoT can significantly enhance the living environment by automating energy distribution and smart energy management systems. Smart embedded technologies and sensors enable the use of the Internet of Things by collecting data that may be utilized to make more informed decisions, while avoiding performance degradation and developing intelligent systems. This Special Issue aims to publish original, unpublished technical research findings on topics such as smart solar energy systems, wind turbines, hybrid systems, the energy efficiency of fault detection and diagnostic tools, and machine learning techniques used in predicting the future. Artificial intelligence approaches, such as fuzzy logic, meta-heuristic algorithms, time series analysis, and hybrid methods, are used in all these domains and more.

We welcome contributions ranging from theory to applications and case studies linked to the development and performance of smart embedded sensing devices for sustainable renewable energy and system applications. Studies and survey papers are also encouraged, as long as they provide novel perspectives based on an in-depth examination and synthesis of the state of the art in the field.

This Special Issue is intended for academics, researchers, and industry professionals hoping to publish their findings on the following topics of interest (among others):

  • Smart supervisory control of grids;
  • Artificial intelligence sensor techniques for IoT use cases;
  • Weather-based solar power prediction;
  • Edge artificial intelligence;
  • Blockchain for sustainable renewable energy and systems;
  • Internet of Things in the renewable energy industry;
  • Smart embedded technologies and sensors for green infrastructure, electricity grids, and healthcare;
  • Smart energy management systems;
  • AI-enabled smart materials;
  • Artificial-intelligence-powered sensor system for sustainable renewable energy systems.

Dr. Manoharan Madhiarasan
Dr. Mohamed Louzazni
Dr. Marco Mussetta
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. 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 2600 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

  • energy system modeling
  • energy informatics
  • integrated energy system
  • interdisciplinary research in energy
  • artificial intelligence
  • edge computing
  • optimization
  • cyber-physical systems
  • mechatronics
  • smart materials
  • blockchain
  • Internet of Things and smart power systems

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Published Papers (1 paper)

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Research

17 pages, 3864 KiB  
Article
Advanced Energy Management Strategy of Photovoltaic/PEMFC/Lithium-Ion Batteries/Supercapacitors Hybrid Renewable Power System Using White Shark Optimizer
by Hesham Alhumade, Hegazy Rezk, Mohamed Louzazni, Iqbal Ahmed Moujdin and Saad Al-Shahrani
Sensors 2023, 23(3), 1534; https://doi.org/10.3390/s23031534 - 30 Jan 2023
Cited by 9 | Viewed by 2230
Abstract
The slow dynamic response of a proton exchange membrane fuel cell (PEMFC) to high load change during deficit periods must be considered. Therefore, integrating the hybrid system with energy storage devices like battery storage and/or a supercapacitor is necessary. To reduce the consumed [...] Read more.
The slow dynamic response of a proton exchange membrane fuel cell (PEMFC) to high load change during deficit periods must be considered. Therefore, integrating the hybrid system with energy storage devices like battery storage and/or a supercapacitor is necessary. To reduce the consumed hydrogen, an energy management strategy (EMS) based on the white shark optimizer (WSO) for photovoltaic/PEMFC/lithium-ion batteries/supercapacitors microgrid has been developed. The EMSs distribute the load demand among the photovoltaic, PEMFC, lithium-ion batteries, and supercapacitors. The design of EMSs must be such that it minimizes the use of hydrogen while simultaneously ensuring that each energy source performs inside its own parameters. The recommended EMS-based-WSO was evaluated in regard to other EMSs regarding hydrogen fuel consumption and effectiveness. The considered EMSs are state machine control strategy (SMCS), classical external energy maximization strategy (EEMS), and optimized EEMS-based particle swarm optimization (PSO). Thanks to the proposed EEMS-based WSO, hydrogen utilization has been reduced by 34.17%, 29.47%, and 2.1%, respectively, compared with SMCS, EEMS, and PSO. In addition, the efficiency increased by 6.05%, 9.5%, and 0.33%, respectively, compared with SMCS, EEMS, and PSO. Full article
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