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Artificial Intelligence on Energy 4.0
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Artificial Intelligence on Energy 4.0

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 (30 June 2021) | Viewed by 6278

Special Issue Editors

Dr. Miguel Delgado-Prieto

E-Mail Website
Guest Editor
Automatic Control Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Barcelona, Spain
Interests: industry 4.0 and digital transformation; condition-based monitoring; predictive maintenance and control; cyber–physical systems and digital twins
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Romeral

E-Mail Website
Guest Editor
Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
Interests: electromobility; Industry 4.0; energy efficiency

Special Issue Information

Dear Colleagues,

The digitalization of energy is breaking the traditional boundaries between supply and demand. The energy sector, based on data and artificial intelligence, is increasingly decentralized—redefining the generation, transport, distribution, and consumption models of so-called Energy 4.0. Energy 4.0 is, then, a matter of the integration of energy sources and energy demand, particularly considering the manufacturing sector as one of the main players in the paradigm. Effective deployment of the fourth industrial revolution entails energy management as a main driver of the factories of the future. Thus, in classical automation, energy is considered an input vector but is not integrated into the factory operation strategy. Current digitization, connectivity, and processing technologies allow energy to become a production variable of the companies, while the factory is an active element of the smart grid distribution system. In this regard, the energy sector has embraced emerging technologies such as the Internet of Things, data science, machine/deep learning, and cloud computing. It has mainly focused on the deployment of cyberphysical-based solutions towards sustainable and operationally efficient strategies for producing and delivering energy, and taken advantage of the digital transformation of process automation in plants.

This Special Issue considers the digitalization of the energy and utilities sector towards Energy 4.0, and how these transformations, supported by artificial intelligence technology, will impact the factories of the future. Papers considering the following areas of research are invited:

  • Cyberphysical systems applied to energy sector;
  • Optimization and control of power generation plants;
  • Artificial intelligence-based demand response;
  • Predictive maintenance and machine learning applied to the energy and utilities sector;
  • Industrial Internet of Things in energy management;
  • Industrial smart grids: transportation, distribution and energy storage.
Prof. Dr. Miguel Delgado-Prieto
Prof. Dr. Luis Romeral
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 quality
  • energy flow
  • energy storage
  • distributed generation
  • energy infrastructure
  • power plants
  • energy conversion and management
  • deep learning
  • cyberphysical systems
  • artificial intelligence and machine learning
  • energy distribution
  • demand side and utilities
  • renewables
  • predictive maintenance
  • decision support systems
  • information fusion
  • industrial demand response
  • industrial demand-side management strategies

Published Papers (2 papers)

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Research

17 pages, 1462 KiB  
Article
A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances
by Artvin-Darien Gonzalez-Abreu, Miguel Delgado-Prieto, Roque-Alfredo Osornio-Rios, Juan-Jose Saucedo-Dorantes and Rene-de-Jesus Romero-Troncoso
Energies 2021, 14(10), 2839; https://doi.org/10.3390/en14102839 - 14 May 2021
Cited by 20 | Viewed by 1998
Abstract
Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research [...] Read more.
Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization. Full article
(This article belongs to the Special Issue Artificial Intelligence on Energy 4.0)
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18 pages, 2341 KiB  
Article
Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics
by Waqar Muhammad Ashraf, Ghulam Moeen Uddin, Muhammad Farooq, Fahid Riaz, Hassan Afroze Ahmad, Ahmad Hassan Kamal, Saqib Anwar, Ahmed M. El-Sherbeeny, Muhammad Haider Khan, Noman Hafeez, Arman Ali, Abdul Samee, Muhammad Ahmad Naeem, Ahsaan Jamil, Hafiz Ali Hassan, Muhammad Muneeb, Ijaz Ahmad Chaudhary, Marcin Sosnowski and Jaroslaw Krzywanski
Energies 2021, 14(5), 1227; https://doi.org/10.3390/en14051227 - 24 Feb 2021
Cited by 20 | Viewed by 3382
Abstract
Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed [...] Read more.
Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator’s power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management. Full article
(This article belongs to the Special Issue Artificial Intelligence on Energy 4.0)
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