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Artificial Intelligence Technologies for Industrial and Energy Systems
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Artificial Intelligence Technologies for Industrial and Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (21 February 2022) | Viewed by 22359

Special Issue Editors

Prof. Dr. Elena Gaudioso Vázquez

E-Mail Website
Guest Editor
Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, Spain
Interests: artificial intellgence in industrial applications, recommender systems, adaptive systems, and machine learning
Dr. Félix Hernández del Olmo

E-Mail Website
Guest Editor
Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, Spain
Interests: machine learning; reinforcement learning; adaptive control; recommender systems; user modeling;wastewater systems; adaptive predictive control; adaptive interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy demand worldwide grows every year. Thus, there is a great interest in reducing energy consumption (both domestic and industrial) and in optimizing energy supply systems. The amount of data available from industrial systems or domestic buildings can be used to prevent faults or to optimize production in energy systems. An additional important goal is to use these data to optimize maintenance and control strategies with the goal of reducing energy consumption in industrial applications or in domestic buildings.

Artificial intelligence systems can make use of the available data to address these goals. This Special Issue invites orginal papers that address (but are not limited to) the following purposes:

  • Artificial Intelligence for industrial process optimization;
  • Optimization of industrial applications and energy systems;
  • Optimization of energy cost savings in industrial applications;
  • Big data for industrial and energy systems;
  • Artificial intelligence for renewable energies.

Prof. Dr. Elena Gaudioso Vázquez
Prof. Dr. Félix Hernández del Olmo
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 intelligence
  • Machine learning
  • Fault prediction
  • Intelligent control
  • Big data
  • Intelligent soft sensors
  • Energy and industrial systems
  • Renewable energies

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Published Papers (4 papers)

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Research

19 pages, 4133 KiB  
Article
Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling
by Yuanhao Shi, Mengwei Li, Jie Wen, Yanru Yang, Fangshu Cui and Jianchao Zeng
Energies 2021, 14(13), 4000; https://doi.org/10.3390/en14134000 - 2 Jul 2021
Cited by 7 | Viewed by 2624
Abstract
Ash fouling has been an important factor in reducing the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating a reasonable soot blowing strategy to [...] Read more.
Ash fouling has been an important factor in reducing the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating a reasonable soot blowing strategy to improve energy efficiency. This study presented a comprehensive approach of dynamic prediction of the ash fouling of heat transfer surfaces in coal-fired power plant boilers. At first, the cleanliness factor is used to reflect the fouling level of the heat transfer surfaces. Then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). To construct a reasonable network model, the minimum information criterion and trial-and-error method are used to determine the delay orders and hidden layers. Finally, the experimental object is established on the 300 MV economizer clearness factor dataset of the power station, and the root mean square error and mean absolute percentage error of the proposed method are the smallest. In addition, the experimental results show that this multiscale prediction model is more competitive than the Elman model. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Industrial and Energy Systems)
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35 pages, 6631 KiB  
Article
Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification
by Andrzej Szajna, Mariusz Kostrzewski, Krzysztof Ciebiera, Roman Stryjski and Waldemar Woźniak
Energies 2021, 14(12), 3659; https://doi.org/10.3390/en14123659 - 19 Jun 2021
Cited by 9 | Viewed by 13400
Abstract
Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes [...] Read more.
Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes or redefinition of the production concepts is meaningful in light of the current industrial and research agendas. Both the mentioned optimization and redefinition are considered in numerous subtopics and technologies. One of the most significant topics in these areas is the newest findings and applications of artificial intelligence (AI)—machine learning (ML) and deep convolutional neural networks (DCNNs). The authors invented a method and device that supports the wiring assembly in the control cabinet production process, namely, the Wire Label Reader (WLR) industrial system. The implementation of this device was a big technical challenge. It required very advanced IT technologies, ML, image recognition, and DCNN as well. This paper focuses on an in-depth description of the underlying methodology of this device, its construction, and foremostly, the assembly industrial processes, through which this device is implemented. It was significant for the authors to validate the usability of the device within mentioned production processes and to express both advantages and challenges connected to such assembly process development. The authors noted that in-depth studies connected to the effects of AI applications in the presented area are sparse. Further, the idea of the WLR device is presented while also including results of DCNN training (with recognition results of 99.7% although challenging conditions), the device implementation in the wire assembly production process, and its users’ opinions. The authors have analyzed how the WLR affects assembly process time and energy consumption, and accordingly, the advantages and challenges of the device. Among the most impressive results of the WLR implementation in the assembly process one can be mentioned—the device ensures significant process time reduction regardless of the number of characters printed on a wire. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Industrial and Energy Systems)
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22 pages, 386 KiB  
Article
Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems
by Alejandro Santiago, Mirna Ponce-Flores, J. David Terán-Villanueva, Fausto Balderas, Salvador Ibarra Martínez, José Antonio Castan Rocha, Julio Laria Menchaca and Mayra Guadalupe Treviño Berrones
Energies 2021, 14(12), 3473; https://doi.org/10.3390/en14123473 - 11 Jun 2021
Cited by 2 | Viewed by 2259
Abstract
The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in [...] Read more.
The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle energy consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Industrial and Energy Systems)
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22 pages, 4248 KiB  
Article
Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources
by Santiago Bañales, Raquel Dormido and Natividad Duro
Energies 2021, 14(12), 3458; https://doi.org/10.3390/en14123458 - 11 Jun 2021
Cited by 10 | Viewed by 3077
Abstract
The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency [...] Read more.
The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency energy use time series data to segment electricity customers according to their demand response potential. This paper proposes a smart meter time series clustering methodology based on a two-stage k-medoids clustering of normalized load-shape time series organized around the day divided into 48 time points. Time complexity is drastically reduced by first applying the k-medoids on each customer separately, and second on the total set of customer representatives. Further time complexity reduction is achieved using time series representation with low computational needs. Customer segmentation is undertaken with only four easy-to-interpret features: average energy use, energy–temperature correlation, entropy of the load-shape representative vector, and distance to wind generation patterns. This last feature is computed using the dynamic time warping distance between load and expected wind generation shape representative medoids. The two-stage clustering proves to be computationally effective, scalable and performant according to both internal validity metrics, based on average silhouette, and external validation, based on the ground truth embedded in customer surveys. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Industrial and Energy Systems)
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