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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (22 June 2023) | Viewed by 9624

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Special Issue Editors

Dr. Mateus Mendes

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Guest Editor

Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: machine learning, data analysis, computer vision in energy field
Special Issues, Collections and Topics in MDPI journals

Dr. Inácio Fonseca

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Guest Editor

Polytechnic of Coimbra – ISEC, 3030-199 Coimbra, Portugal
Interests: automatic development of (web) applications through AI; intelligent systems in maintenance and electrical engineering; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From objects associated with the sun and sky, to nano-sensors in small animals, modern civilization revolves around data, producing gigabytes every second. The whole system can be visualized as a river of pulsating bytes, flowing into modern data centers, data lakes, and data warehouses that utilize terawatts of power.

It is our aim and duty to ensure that data are ethically harvested, safely stored, and correctly used to extract information and knowledge.

Increasingly smart approaches to harvesting energy and data, faster networks, and smaller devices, feed and expand the human senses, and intertwine with and enhance human lives in the modern world, which is increasingly more virtual, less physical, and more about energy, data, and connections.

The Internet of Things will consist of billions of smart appliances, draining small amounts of power from the electrical grid and feeding terabytes of data to the internet. Electric vehicles are expected to add additional strain to an already complex power grid, which increasingly relies on micro-production from renewable sources. Intelligent and efficient grid management algorithms are urgently required for the smart grid of the future.

The sole topic of this Special Issue is data, and the wondrous process by which the world becomes increasingly more digital. From the use of smart devices to the latest data analysis algorithms, we are feeding, sensing, and shaping a world of data. We invite you to contribute your work and views regarding the processes of efficiently producing and harvesting energy and data; monitoring processes; transferring and storing data; interoperability; data pre-processing; image and text classification; augmented and virtual reality; and related topics.

The scope includes, but is not limited to, the following:

Augmented and virtual reality;
Data analysis;
Energy consumption forecast;
Energy production forecast;
Impact of digitization on institutions;
Impact of energy demand and production on social activity;
Intelligent automation systems;
Intelligent data processing;
Intelligent sensors for real-time decisions;
Intelligent system integration and inter-operation;
IoT;
On-demand smart decisions;
Smart embedded systems;
Smart energy use at home and in industries.

Dr. Mateus Mendes
Dr. Inácio Fonseca
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

artificial neural networks
augmented reality
computer vision
condition monitoring
data farming
data mining
deep learning
object detection
nanotechnology
pattern recognition
predictive maintenance
time series analysis

Published Papers (4 papers)

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Research

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17 pages, 2102 KiB

Open AccessArticle

Counting People and Bicycles in Real Time Using YOLO on Jetson Nano

by Hugo Gomes, Nuno Redinha, Nuno Lavado and Mateus Mendes

Energies 2022, 15(23), 8816; https://0-doi-org.brum.beds.ac.uk/10.3390/en15238816 - 22 Nov 2022

Cited by 7 | Viewed by 3020

Abstract

Counting objects in video images has been an active area of computer vision for decades. For precise counting, it is necessary to detect objects and follow them through consecutive frames. Deep neural networks have allowed great improvements in this area. Nonetheless, this task is still a challenge for edge computing, especially when low-power edge AI devices must be used. The present work describes an application where an edge device is used to run a YOLO network and V-IOU tracker to count people and bicycles in real time. A selective frame-downsampling algorithm is used to allow a larger frame rate when necessary while optimizing memory usage and energy consumption. In the experiments, the system was able to detect and count the objects with 18 counting errors in 525 objects and a mean inference time of 112.82 ms per frame. With the selective downsampling algorithm, it was also capable of recovering and reduce memory usage while maintaining its precision. Full article

(This article belongs to the Special Issue Digitization of Energy Supply and Demand Sides)

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Review

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38 pages, 569 KiB

Open AccessFeature PaperReview

Deep Neural Networks in Power Systems: A Review

by Mahdi Khodayar and Jacob Regan

Energies 2023, 16(12), 4773; https://0-doi-org.brum.beds.ac.uk/10.3390/en16124773 - 17 Jun 2023

Cited by 1 | Viewed by 2262

Abstract

Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study’s empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems. Full article

(This article belongs to the Special Issue Digitization of Energy Supply and Demand Sides)

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21 pages, 789 KiB

Open AccessReview

Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

by Nurkamilya Daurenbayeva, Almas Nurlanuly, Lyazzat Atymtayeva and Mateus Mendes

Energies 2023, 16(8), 3508; https://0-doi-org.brum.beds.ac.uk/10.3390/en16083508 - 18 Apr 2023

Cited by 1 | Viewed by 1636

Abstract

An appropriate microclimate is one of the most important factors of a healthy and comfortable life. The microclimate of a place is determined by the temperature, humidity and speed of the air. Those factors determine how a person feels thermal comfort and, therefore, they play an essential role in people’s lives. Control of microclimate parameters is a very important topic for buildings, as well as greenhouses, where adequate microclimate is fundamental for best-growing results. Microclimate systems require adequate monitoring and maintenance, for their failure or suboptimal performance can increase energy consumption and have catastrophic results. In recent years, Fault Detection and Diagnosis in microclimate systems have been paid more attention. The main goal of those systems is to effectively detect faults and accurately isolate them to a failing component in the shortest time possible. Sometimes it is even possible to predict and anticipate failures, which allows preventing the failures from happening if appropriate measures are taken in time. The present paper reviews the state of the art in fault detection and diagnosis methods. It shows the growing importance of the topic and highlights important open research questions. Full article

(This article belongs to the Special Issue Digitization of Energy Supply and Demand Sides)

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Other

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32 pages, 538 KiB

Open AccessEditor’s ChoiceSystematic Review

A Review of Cybersecurity Concerns for Transactive Energy Markets

by Daniel Sousa-Dias, Daniel Amyot, Ashkan Rahimi-Kian and John Mylopoulos

Energies 2023, 16(13), 4838; https://0-doi-org.brum.beds.ac.uk/10.3390/en16134838 - 21 Jun 2023

Cited by 2 | Viewed by 1688

Abstract

Advances in energy generation and distribution technology have created the need for new power management paradigms. Transactive energy markets are integrated software and hardware systems that enable optimized energy management and direct trading between prosumers. This literature review covers unresolved security and privacy vulnerabilities in the proposed implementations of such markets. We first performed a coarse search for such implementations. We then combed the resulting literature for references to privacy concerns, security vulnerabilities, and attacks that their system was either vulnerable to or sought to address. We did so with a particular focus on threats that were not mitigated by the use of blockchain technology, a commonly employed solution. Based on evidence from 28 peer-reviewed papers, we synthesized 14 categories of concerns and their proposed solutions. We found that there are some concerns that have been widely addressed, such as protecting trading history when using a public blockchain. Conversely, there were serious threats that are not sufficiently being considered. While a lack of real-world deployment has limited information about which attacks are most likely or feasible, there are clear areas of priority that we recommend to address going forward, including market attacks, false data injection attacks, single points of failure, energy usage data leakage, and privacy. Full article

(This article belongs to the Special Issue Digitization of Energy Supply and Demand Sides)

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