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Energies
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Effective Energy Use in Devices and Applications for IoT
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Effective Energy Use in Devices and Applications for IoT

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (6 September 2023) | Viewed by 2343

Special Issue Editors

Dr. Krzysztof Przystupa

E-Mail Website
Guest Editor
Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Interests: safety and reliability engineering; network traffic analysis; internet of things; control theory; automation and control systems; control systems; stability; electrical drives; power transfer; measuring technology; measuring transducers; mathematical analysis in engineering; scheduling of experiments; statistical analysis; renewable energy sources
Prof. Dr. Orest Kochan

E-Mail Website
Guest Editor
Department of Measuring-Information Technologies, Lviv Polytechnic National University, Bandera Str.12, 79012 Lviv, Ukraine
Interests: network traffic analysis; Internet of Things; measurements; temperature measurements; modelling; artificial inteligence; effective energy use in IoT; energy-efficient design for IoT; statistical analysis; regression analysis

Special Issue Information

Dear Colleagues,

It is almost impossible to imagine a modern society without devices constantly connected to the Internet. The number of such devices is constantly growing, not only in the conventional fields of application, but also in new areas, including smart grids, smart houses, smart transportation, autonomous vehicles, robotics, etc. This growth has called for advances in the materials, design techniques, algorithms, modelling tools and manufacturing technologies of modern devices and gadgets. The concept of the Internet of Things further revolutionized many habitual areas of human life. However, devices interconnected in networks are heavily dependent on electric power. Therefore, their proper operation depends on effective energy use, especially when undergoing the transition from unidirectional power supply patterns to a multi-energy market. In general, the planning of multi-layer heterogeneous networks with complex topology is an important task, because the energy efficiency of integrated technologies needs to be improved. Heterogeneous networks of energies have further accelerated the revolution of energy structure and operational modes of IoT systems and devices. Thus, the combination of energy-efficient planning and the diversification of energy sources for IoT represents an effective way to resolve the global energy crisis and climate change by reducing carbon emissions. The ongoing scientific revolution in the field of IoT is affecting and integrating various types of networks, systems, and devices, leading to the need for the coordination of energy generation, energy distribution, the operation of energy systems, and the IoT system, as well as separate devices as a single bundle. This requires the study of aspects of energy efficiency in IoT, especially with respect to planning energy-efficient algorithms, structure designs, and promising techniques for real-world applications that will improve energy efficiency at various scales, from separate devices to large systems.

Therefore, this Special Issue is focused on studies presenting new ideas, advanced theories, and experimental results in the field of the energy-efficient operation of modern IoT systems.

Dr. Krzysztof Przystupa
Prof. Dr. Orest Kochan
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. Energies 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

  • IoT
  • energy efficiency in IoT
  • smart grid
  • data acquisition 
  • measurements in IoT
  • metrology in IoT
  • modelling in the field of energy-efficient systems for IoT 
  • cascaded and modular multilevel converters
  • control methods in electronic systems 
  • applications for electric machines control
  • modulation methods in power electronic systems

Published Papers (2 papers)

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Research

27 pages, 16439 KiB  
Article
Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases
by Joanna Michalowska
Energies 2024, 17(1), 126; https://doi.org/10.3390/en17010126 - 25 Dec 2023
Viewed by 676
Abstract
Tests on the content of the electrical component of the electromagnetic field (EMF) were carried out with an NHT3DL broadband meter by Microrad using a 01E (100 kHz ÷ 6.5 GHz) measuring probe. Measurements were made during training flights (Cessna C172, Cessna C152, [...] Read more.
Tests on the content of the electrical component of the electromagnetic field (EMF) were carried out with an NHT3DL broadband meter by Microrad using a 01E (100 kHz ÷ 6.5 GHz) measuring probe. Measurements were made during training flights (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T aircrafts). A neural network was used, the task of which was to learn to predict the successive values of average (ERMS) and instantaneous (EPEAK) electromagnetic fields used here. Such a solution would make it possible to determine the most favorable routes for all aircrafts. This article presents a model of an artificial neural network which aims to predict the intensity of the electrical component of the electromagnetic field. In order to create the developed model, that is, to create a training sequence for the model, a series of measurements was carried out on four types of aircraft (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T). The model was based on long short-term memory (LSTM) layers. The tests carried out showed that the accuracy of the model was higher than that of the reference method. The developed model was able to estimate the electrical component for the vicinity of the routes on which it was trained in order to optimize the exposure of the aircraft to the electrical component of the electromagnetic field. In addition, it allowed for data analysis of the same training flight routes. The reference point for the obtained electric energy results were the normative limits of the electromagnetic field that may affect the crew and passengers during a flight. Monitoring and measuring the electromagnetic field generated by devices is important from an environmental point of view, as well as for the purposes of human body protection and electromagnetic compatibility. In order to improve reliability in general aviation and to adapt to the proposed requirements, aviation training centers are obliged to introduce systems for supervising and analyzing flight parameters. Full article
(This article belongs to the Special Issue Effective Energy Use in Devices and Applications for IoT)
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15 pages, 2676 KiB  
Article
Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems
by Zhang Yiming, Xie Fang, Olena Hordiichuk-Bublivska, Halyna Beshley and Mykola Beshley
Energies 2023, 16(16), 5996; https://doi.org/10.3390/en16165996 - 16 Aug 2023
Viewed by 995
Abstract
The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To [...] Read more.
The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems. Full article
(This article belongs to the Special Issue Effective Energy Use in Devices and Applications for IoT)
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