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Demand-Side Energy Management Optimization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 12 December 2024 | Viewed by 1804

Special Issue Editors


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Guest Editor
Energy ICT Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea
Interests: demand-side energy management optimization; demand–supply energy balancing; distributed energy resources (DERs) management technology; energy broker mechanism for small-scale DERs; low-end standard platform for factory energy management system (FEMS)
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Engineering, Daejeon University, Daejeon 34520, Republic of Korea
Interests: 5G; smart grid; off-grid; IoT; distributed energy resources; smart buildings; resource allocation; energy distribution; demand response; optimization; game theory; machine learning; artificial intelligence; blockchain
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. School of Computing and Informatics, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong BE 1410, Brunei
2. KAIST Institute for Information Technology Convergence, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
Interests: edge computing; Internet of Things; green networking; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

"Demand-Side Energy Management Optimization", a Special Issue in Energies, is now open for submissions. The global energy demand is expected to grow significantly over the coming years. Therefore, it has become increasingly important to maximize the utilization of energy generated from different sources considering performance requirements. This requires a more in-depth understanding of energy demand behavior, user requirements, energy generation patterns, energy balancing, energy trading and grid stability monitoring. Undoubtedly, the growing use of sensors and actuators in grids as well as the unprecedented improvement in smart grid communication infrastructure, data storage and computational support leveraged by cloud and edge computing have paved the way for more advanced demand-side energy management. Due to the need to reduce carbon emissions from fossil-fuel-based energy generation systems, we also have witnessed an increasing use of renewable energy sources for energy generation, and efforts to optimize demand energy use. Therefore, a better understanding of renewable energy generation behavior and efficient grid integration are crucial for sustainable energy use. Additionally, because of climate change and global conflicts, the current energy trade, which aims to expand green energy, is being challenged, so we must constantly strive to solve these problems with a variety of applications, domains and perspectives. New and revolutionary ideas in terms of optimization technologies, demand management schemes, network and communication interactions and opportunities for new businesses according to the current paradigm shift in energy systems are encouraged.

This Special Issue will address novel and innovative contributions investigating the optimization of various demand management flexibilities. Topics of interest for publication include, but are not limited to:

  • Demand-side energy management;
  • Greening demand-side energy management;
  • Distributed energy resources management;
  • Energy demand and supply integrated management;
  • Demand-aware energy scheduling;
  • Demand-aware energy storage optimization;
  • Demand-aware power quality improvement techniques;
  • Demand optimization;
  • Home, building and factory energy management;
  • Energy trading;
  • Load scheduling;
  • Smart grid stability;
  • Internet of Things;
  • IoT-based grid monitoring;
  • Edge computing for demand-side energy management;
  • AI for demand prediction;
  • AI for demand prioritization;
  • Carbon neutrality;
  • Climate change.

Dr. Yoon-Sik Yoo
Dr. Seung Hyun Jeon
Dr. S. H. Shah Newaz
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

  • energy management
  • greening demand-side energy management
  • demand-aware
  • demand prioritization
  • energy scheduling
  • renewable energy resources management
  • virtual power plants management
  • optimization technology
  • energy efficiency
  • dynamic pricing
  • cost minimization
  • maximization of renewable energy management
  • welfare maximization
  • game theory
  • artificial intelligence
  • machine learning

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Published Papers (1 paper)

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Research

12 pages, 1448 KiB  
Article
ML- and LSTM-Based Radiator Predictive Maintenance for Energy Saving in Compressed Air Systems
by Seung Hyun Jeon, Sarang Yoo, Yoon-Sik Yoo and Il-Woo Lee
Energies 2024, 17(6), 1428; https://doi.org/10.3390/en17061428 - 15 Mar 2024
Viewed by 1136
Abstract
Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable [...] Read more.
Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%. Full article
(This article belongs to the Special Issue Demand-Side Energy Management Optimization)
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