Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Challenge and Research Trends of Artificial Neural Network
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Challenge and Research Trends of Artificial Neural Network

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 April 2022) | Viewed by 6232

Special Issue Editor

Dr. Konrad Zajkowski

E-Mail Website
Guest Editor
Department of Energy, Faculty of Mechanical Engineering, Technical University of Koszalin, Koszalin, Poland
Interests: practical and theoretical experience in the field of electronics; electrotechnics and energetics; artificial neural networks; technical diagnostics; power theory; energy systems; electric drives; reactive power compensation and overvoltage reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, artificial neural networks are used in many fields of technology. This is due to the need to optimize energy consumption in the design, production and operation process. Importantly, the use of autonomy in these processes leads to a reduction in operating costs. Performing technical diagnostics of the condition of the device leads to earlier detection of failures. As a result, failure-free operation time is extended, which reduces production costs. It also becomes easier to ensure continuity of product supply.

The background and aims of this Special Issue focus on the research challenges and trends that are commonly presented in Energies, as well as aspects relating to artificial neural networks. Therefore, articles discussing the above problems concerning all stages of production and operation of technological devices will be welcome. For the reader, the materials describing completed concepts that have already been implemented, or that are on the verge of implementation, will undoubtedly be of particular value.

Dr. Konrad Zajkowski
Guest Editor

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

  • neural network
  • artificial intelligence
  • energy systems
  • technical diagnostics
  • energy saving

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 1991 KiB  
Article
Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite
by Radosław Puka, Bartosz Łamasz and Marek Michalski
Energies 2021, 14(24), 8494; https://doi.org/10.3390/en14248494 - 16 Dec 2021
Cited by 1 | Viewed by 2000
Abstract
During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and demand, notably oil demand and pricing have become even more unpredictable than before. Thus, for companies that buy large quantities of oil, effective oil price risk management is [...] Read more.
During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and demand, notably oil demand and pricing have become even more unpredictable than before. Thus, for companies that buy large quantities of oil, effective oil price risk management is crucial for business success. Nevertheless, businesses’ risk appetite, specifically willingness to accept more risk to achieve desired business benefits, varies significantly. The aim of this paper is to deepen the analysis of the effectiveness of employing artificial neural networks (ANNs) in hedging against oil price changes by searching for buy signals for European WTI (West Texas Intermediate) crude oil call options, while taking into account the level of risk appetite. The number of generated buy signals decreases with increasing risk appetite, and thus the amount of capital necessary to buy options decreases. However, the results show that fewer buy signals do not necessarily translate into lower returns generated by networks in a given class. Thus, higher levels of return on the purchase of call options may be obtained. The conducted analyses clearly proved that ANNs can be a useful tool in the process of managing WTI crude oil price change risk. Using the analyzed network parameters, up to 29.9% of the theoretical maximum possible profit from buying options every day was obtained in the test set. Furthermore, all proposed networks generated some profit for the test set. The values of all indicators used in the analyses confirm that the ANNs can be effective regardless of the level of risk appetite, so in this respect they may be described as a universal decision support tool. Full article
(This article belongs to the Special Issue Challenge and Research Trends of Artificial Neural Network)
►▼ Show Figures

Figure 1

18 pages, 3116 KiB  
Article
Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices
by Stanisław Duer, Jan Valicek, Jacek Paś, Marek Stawowy, Dariusz Bernatowicz, Radosław Duer and Marcin Walczak
Energies 2021, 14(9), 2719; https://doi.org/10.3390/en14092719 - 10 May 2021
Cited by 7 | Viewed by 1592
Abstract
The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the [...] Read more.
The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {12VL}. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {23VL}—serviceability condition and {13VL}—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object’s elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement. Full article
(This article belongs to the Special Issue Challenge and Research Trends of Artificial Neural Network)
►▼ Show Figures

Figure 1

19 pages, 4834 KiB  
Article
Examination of Multivalent Diagnoses Developed by a Diagnostic Program with an Artificial Neural Network for Devices in the Electric Hybrid Power Supply System “House on Water”
by Stanisław Duer, Konrad Zajkowski, Marta Harničárová, Henryk Charun and Dariusz Bernatowicz
Energies 2021, 14(8), 2153; https://doi.org/10.3390/en14082153 - 12 Apr 2021
Cited by 18 | Viewed by 1674
Abstract
This article presents the problem of diagnostic examination by the (DIAG) diagnostic system of devices of the House on Water (HoW) hybrid electric power system in the multi-valued (2, 3, and 4) state assessment. Forming the basis for the functioning of the (DIAG) [...] Read more.
This article presents the problem of diagnostic examination by the (DIAG) diagnostic system of devices of the House on Water (HoW) hybrid electric power system in the multi-valued (2, 3, and 4) state assessment. Forming the basis for the functioning of the (DIAG) diagnostic system is the measurement knowledge base of the object tested. For this purpose, the issues of building a diagnostic knowledge base for a hybrid power system for HoW are presented. The basis for obtaining diagnostic information for the measurement knowledge base is a functional and diagnostic analysis of the hybrid power system tested. The result of this analysis is a functional and diagnostic model of the research object. At the next stage of the work, on the basis of the model created, the sets of basic elements and the sets of measurement signals were determined together with the reference signals assigned. State classification in the (DIAG) system is based on an analysis of the value of the divergence metrics of the signal vectors tested. The purpose of the HoW diagnostic test is to assess an increase in the diagnoses developed by the intelligent diagnostic system (DIAG 2) in 4-valued logic in relation to the assessments in 3- and 2-valued logic. Full article
(This article belongs to the Special Issue Challenge and Research Trends of Artificial Neural Network)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop