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Energy Management Systems Based on Industrial Artificial Intelligence

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 576

Special Issue Editors


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Guest Editor
Department of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, Greece
Interests: computational-artificial intelligence; intelligent control; evolutionary computation; neural networks; systems optimization; distributed artificial intelligence; multi-agent management systems; hybrid intelligent systems and development intelligent systems in biomedicine and energy management in buildings/hospitals
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Natural Resources and Agricultural, Agricultural University of Athens, 10679 Athens, Greece
Interests: renewable energy technology (including solar photovoltaics and microgrids for rural and remote areas); sustainable technologies for agriculture; energy efficiency

Special Issue Information

Dear Colleague,

Artificial intelligence (AI) has dramatically altered the dynamic landscape of science and industry in the past several years. Industrial AI (IAI), defined as “ a systematic discipline, which focuses on developing, validating, and deploying various machine learning algorithms for industrial applications with sustainable performance,” can power the sustainable performance of energy management systems (EMSs) and the critical aspect of optimizing energy distribution. IAI can actualize smart and resilient industrial EMSs that are fault-tolerant, self-organizing, and can predict potential breakdowns in critical energy infrastructure. IAI focuses on developing EMSs for the management of renewable energy in smart grids and the challenge of managing the additional load on grids due to electric vehicles (EVs).

This Special Issue aims to present recent and high-quality research on the development of AI techniques, such as optimized solutions, industrial data modeling, and control, for improving industrial EMSs and building EMSs (BEMSs).

Topics of interest for publication include, but are not limited to, the following:

  • IAI in demand-side management (DSM)
  • IAI in building energy management system (BEMS)
  • IAI and IoT energy management systems
  • Forecast energy demand by AI
  • Identify inefficiencies in EMS by AI
  • Energy optimization by AI
  • IAI in renewable energy systems
  • IAI in energy hubs and microgrids
  • IAI in smart grids
  • IAI in smart cities
  • IAI for sustainable EMS
  • Energy trading with AI
  • Prediction of renewable energy by AI
  • Estimation of occupancy in buildings by AI
  • IAI in electric vehicles
  • Fuzzy logic energy management system
  • Decision methods in EMS with AI 

Prof. Dr. Anastasios Dounis
Prof. Dr. George Papadakis
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 system
  • industrial AI
  • ambient intelligence
  • distributed AI systems
  • embedded AI
  • multi-agent systems
  • intelligent control
  • decision-making
  • machine learning
  • deep learning
  • field-programmable gate arrays (FPGAs) in AI systems
  • fuzzy systems

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Published Papers (1 paper)

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Research

15 pages, 11458 KiB  
Article
Energy Optimization in Ultrasound Tomography Through Sensor Reduction Supported by Machine Learning Algorithms
by Bartłomiej Baran, Tomasz Rymarczyk, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Cieplak, Marcin Gąsior, Marcin Marczuk, Edmund Wąsik and Konrad Gauda
Energies 2024, 17(21), 5406; https://doi.org/10.3390/en17215406 - 30 Oct 2024
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Abstract
This paper focuses on reducing energy consumption in ultrasound tomography by utilizing machine learning techniques. The core idea is to investigate the feasibility of minimizing the number of measurement sensors without sacrificing prediction accuracy. This article evaluates the quality of reconstructions derived from [...] Read more.
This paper focuses on reducing energy consumption in ultrasound tomography by utilizing machine learning techniques. The core idea is to investigate the feasibility of minimizing the number of measurement sensors without sacrificing prediction accuracy. This article evaluates the quality of reconstructions derived from data collected through two or three measurement channels. In subsequent steps, machine learning models are developed to predict the number, location, and size of the objects. A reliable object detection method is introduced, requiring less information than traditional signal analysis from multiple channels. Various machine learning models were tested and compared to validate the approach, with most demonstrating high accuracy or R2 scores in their respective tasks. By reducing the number of sensors, the goal is to lower energy usage while maintaining high precision in localization. This study contributes to the ongoing research on energy efficiency in sensing and localization, especially in environments where resource optimization is crucial, such as remote or resource-limited settings. Full article
(This article belongs to the Special Issue Energy Management Systems Based on Industrial Artificial Intelligence)
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