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Electronics
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Smart Energy Systems Using AI and IoT Solutions
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Smart Energy Systems Using AI and IoT Solutions

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 8331

Special Issue Editors

Dr. Luigi Scarcello

E-Mail Website
Guest Editor
ICAR-CNR, Institute for High Performance Computing and Networking of the Italian National Research Council, Via P. Bucci 8/9C, 87036 Rende, CS, Italy
Interests: energy communities; renewable energy systems; smart grid; demand response; building automation; internet of things
Dr. Carlo Mastroianni

E-Mail Website
Guest Editor
ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, Via P. Bucci 8/9C, 87036 Rende, CS, Italy
Interests: parallel and distributed computing; internet of things; cloud computing and data centers; smart grids and quantum computing
Special Issues, Collections and Topics in MDPI journals
Dr. Teemu Leppänen

E-Mail Website
Guest Editor
School of Information Technology, Oulu University of Applied Sciences, 90570 Oulu, Finland
Interests: distributed AI; smart energy efficient IoT and edge computing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of the Special Issue, titled “Smart Energy Systems using AI and IoT Solutions”, is to encourage the submission original papers presenting high-quality research on topics involving smart energy systems in many research fields, e.g., smart buildings, renewable energy, smart grids, energy communities, smart environments, sustainable industry, etc.

Nowadays, the energy transition from fossil-based to zero-carbon sources, fostered by the digitalization of electricity grids and the decentralization of renewable production plants, is transforming the whole energy system. In this context, smart energy systems offer suitable resources and technologies for industry, households and services, which allow the employment of clean energies and provide benefits not only for the climate but also for the economy and society.

As smart energy systems become more complex and pervasive, Artificial Intelligence (AI) and the Internet of Things (IoT) technologies are fundamental in empowering smarter energy environments, delivering numerous solutions in various domains, including energy sensing, metering, communication and management.

In this Special Issue, we aim to investigate the architectures and functionalities of AI and IoT-enabled solutions and prospects in order to improve the effectiveness of smart energy systems.

Furthermore, we aim to highlight how advanced technologies (e.g., edge computing, blockchain, machine learning, swarm intelligence) can complement current energy systems to overcome the existing difficulties and become more efficient, robust and reliable in their operation.

Technical Program Committee Members:

  • Irfanullah Khan  University of Calabria, Italy

Dr. Luigi Scarcello
Dr. Carlo Mastroianni
Dr. Teemu Leppänen
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. Electronics 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.

Keywords

  • IoT
  • renewable energies
  • energy communities
  • smart grid
  • edge computing
  • machine learning

Published Papers (4 papers)

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Research

17 pages, 3798 KiB  
Article
A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services
by Eva González-Romera, Enrique Romero-Cadaval, Carlos Roncero-Clemente, María-Isabel Milanés-Montero, Fermín Barrero-González and Anas-Abdullah Alvi
Electronics 2023, 12(17), 3717; https://doi.org/10.3390/electronics12173717 - 02 Sep 2023
Cited by 1 | Viewed by 973
Abstract
Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a [...] Read more.
Virtual power plants are a useful tool for integrating distributed resources such as renewable generation, electric vehicles, manageable loads, and energy storage systems under a coordinated management system to obtain economic advantages and provide ancillary services to the grid. This study proposes a management system for a residential virtual power plant that includes household loads, photovoltaic generation, energy storage systems, and electric vehicles. With the proposed management system, the virtual power plant is economically optimized (as in commercial virtual power plants) while providing ancillary services (as in technical virtual power plants) to the distribution grid. A genetic algorithm with appropriate constraints is designed and tested to manage the energy storage system and the charge/discharge of electric vehicles, with several economic and technical objectives. Single-objective optimization techniques are compared to multi-objective ones to show that the former perform better in the studied scenarios. A deterministic gradient-based optimization method is also used to validate the performance of the genetic algorithm. The results show that these technical targets (usually reserved for larger virtual power plants) and economic targets can be easily managed in restricted-sized virtual power plants. Full article
(This article belongs to the Special Issue Smart Energy Systems Using AI and IoT Solutions)
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21 pages, 24872 KiB  
Article
Wireless Device with Energy Management for Closed-Loop Deep Brain Stimulation (CLDBS)
by Tiago Matheus Nordi, Gabriel Augusto Ginja, Rodrigo Gounella, Erich Talanoni Fonoff, Eduardo Colombari, Melkzedekue M. Alcântara Moreira, Jose A. Afonso, Vitor Monteiro, Joao L. Afonso and João Paulo Carmo
Electronics 2023, 12(14), 3082; https://doi.org/10.3390/electronics12143082 - 14 Jul 2023
Cited by 1 | Viewed by 1135
Abstract
Deep brain stimulation (DBS) is an effective and safe medical treatment that improves the lives of patients with a wide range of neurological and psychiatric diseases, and has been consolidated as a first-line tool in the last two decades. Closed-loop deep brain stimulation [...] Read more.
Deep brain stimulation (DBS) is an effective and safe medical treatment that improves the lives of patients with a wide range of neurological and psychiatric diseases, and has been consolidated as a first-line tool in the last two decades. Closed-loop deep brain stimulation (CLDBS) pushes this tool further by automatically adjusting the stimulation parameters to the brain response in real time. The main contribution of this paper is a low-size/power-controlled, compact and complete CLDBS system with two simultaneous acquisition channels, two simultaneous neurostimulation channels and wireless communication. Each channel has a low-noise amplifier (LNA) buffer in differential configuration to eliminate the DC signal component of the input. Energy management is efficiently done by the control and communication unit. The battery supports almost 9 h with both the acquisition and stimulation circuits active. If only the stimulation circuit is used as an Open Loop DBS, the battery can hold sufficient voltage for 24 h of operation. The whole system is low-cost and portable and therefore it could be used as a wearable device. Full article
(This article belongs to the Special Issue Smart Energy Systems Using AI and IoT Solutions)
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34 pages, 6151 KiB  
Article
Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities
by Jonne van Dreven, Veselka Boeva, Shahrooz Abghari, Håkan Grahn, Jad Al Koussa and Emilia Motoasca
Electronics 2023, 12(6), 1448; https://doi.org/10.3390/electronics12061448 - 18 Mar 2023
Cited by 5 | Viewed by 3321
Abstract
This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis [...] Read more.
This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating. Full article
(This article belongs to the Special Issue Smart Energy Systems Using AI and IoT Solutions)
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26 pages, 7694 KiB  
Article
An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC
by Mokhtar Jlidi, Faiçal Hamidi, Oscar Barambones, Rabeh Abbassi, Houssem Jerbi, Mohamed Aoun and Ali Karami-Mollaee
Electronics 2023, 12(3), 592; https://doi.org/10.3390/electronics12030592 - 25 Jan 2023
Cited by 5 | Viewed by 2065
Abstract
In recent years, researchers have focused on improving the efficiency of photovoltaic systems, as they have an extremely low efficiency compared to fossil fuels. An obvious issue associated with photovoltaic systems (PVS) is the interruption of power generation caused by changes in solar [...] Read more.
In recent years, researchers have focused on improving the efficiency of photovoltaic systems, as they have an extremely low efficiency compared to fossil fuels. An obvious issue associated with photovoltaic systems (PVS) is the interruption of power generation caused by changes in solar radiation and temperature. As a means of improving the energy efficiency performance of such a system, it is necessary to predict the meteorological conditions that affect PV modules. As part of the proposed research, artificial neural networks (ANNs) will be used for the purpose of predicting the PV system’s current and voltage by predicting the PV system’s operating temperature and radiation, as well as using JAYA-SMC hybrid control in the search for the MPP and duty cycle single-ended primary-inductor converter (SEPIC) that supplies a DC motor. Data sets of size 60538 were used to predict temperature and solar radiation. The data set had been collected from the Department of Systems Engineering and Automation at the Vitoria School of Engineering of the University of the Basque Country. Analyses and numerical simulations showed that the technique was highly effective. In combination with JAYA-SMC hybrid control, the proposed method enabled an accurate estimation of maximum power and robustness with reasonable generality and accuracy (regression (R) = 0.971, mean squared error (MSE) = 0.003). Consequently, this study provides support for energy monitoring and control. Full article
(This article belongs to the Special Issue Smart Energy Systems Using AI and IoT Solutions)
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