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Optimization of Energy Systems Using Intelligent Methods

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 (20 March 2024) | Viewed by 2077

Special Issue Editors


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Guest Editor
Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: machine learning, data analysis, computer vision in energy field
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Polytechnic of Coimbra – ISEC, 3030-199 Coimbra, Portugal
Interests: automatic development of (web) applications through AI; intelligent systems in maintenance and electrical engineering; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy use is fundamental for any civilization. Abundant, safe, and inexpensive energy is the basis for economic development and progress. As civilizations advance, they require increasingly large amounts of energy. On a global scale, however, there is now an effort to move from an oil-centered economy with growing needs to an economy which is more efficient and uses less energy to achieve the same degree of human satisfaction. Ideally, in the future, we will use less energy and still be able to improve human life conditions.

This apparent contradiction is solved by making use of the significant knowledge and technology advances which lead to process optimization, namely those which are achieved mainly through applications of classical and deep machine learning algorithms over large datasets.

 Now that increasingly large amounts of data are available, modern data analysis techniques can be applied in order to extract patterns which would otherwise be impossible to find. This makes it possible to make smarter decisions in different areas, such as energy production, life cycle optimization, energy distribution, and many others.

This Special Issue is mostly dedicated to this topic, whereby modern algorithms are applied in order to optimize systems and processes, from production to consumption, aiming to increase efficiency and profitability while still keeping energy demands low, or by taking advantage of otherwise wasted energy.

  Papers are encouraged in the following areas:

  • Applications of classical machine learning and deep learning;
  • Consumption optimization;
  • Data analysis;
  • Energy efficiency;
  • Energy production;
  • Forecasting;
  • Process and product optimization.

Dr. Mateus Mendes
Dr. Inácio Fonseca
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
  • big data
  • biomass energy
  • energy management
  • systems computer
  • vision convolutional
  • neural networks
  • data analysis
  • deep learning
  • energy forecasting
  • evolutionary algorithms feature engineering
  • IoT
  • machine learning
  • neural networks optimization
  • performance evaluation
  • pollution forecast
  • power safety
  • prediction renewable
  • energy smart
  • energy systems
  • sustainability

Published Papers (2 papers)

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Research

26 pages, 4908 KiB  
Article
Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction
by Basharat Jamil and Lucía Serrano-Luján
Energies 2024, 17(7), 1697; https://doi.org/10.3390/en17071697 - 02 Apr 2024
Viewed by 565
Abstract
In the present work, India’s primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values [...] Read more.
In the present work, India’s primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836. Full article
(This article belongs to the Special Issue Optimization of Energy Systems Using Intelligent Methods)
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18 pages, 1670 KiB  
Article
Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks
by Balduíno César Mateus, José Torres Farinha and Mateus Mendes
Energies 2024, 17(2), 296; https://doi.org/10.3390/en17020296 - 07 Jan 2024
Viewed by 1319
Abstract
Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, [...] Read more.
Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians. Full article
(This article belongs to the Special Issue Optimization of Energy Systems Using Intelligent Methods)
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