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Topical Collection "Artificial Intelligence and Smart Energy"

Editors

Prof. Dr. Wei-Hsin Chen
grade E-Mail Website
Collection Editor
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan
Interests: bioenergy; hydrogen energy; clean energy; environmental engineering; energy management
Special Issues and Collections in MDPI journals
Prof. Dr. Núria Agell
E-Mail Website
Collection Editor
ESADE Business School, Universitat Ramon Llull, Av. Pedralbes 62, Barcelona, Spain
Interests: artificial intelligence; forms of reasoning: qualitative reasoning/fuzzy reasoning; artificial learning; data mining; multi-creiteria and multi-atribute decision-making
Prof. Dr. Zhiyong Liu
E-Mail Website
Collection Editor
The Institute of Computing Technology, Chinese Academy of Sciences, P.O.Box 2704, Beijing 100190, China
Interests: computer architectures and algorithms; parallel and distributed computing; energy efficienct computing
Prof. Dr. Ying-Yi Hong
E-Mail Website
Collection Editor
Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
Interests: power system analysis; artificial intelligence; optimization
Special Issues and Collections in MDPI journals

Topical Collection Information

Dear Colleagues,

We are inviting submissions to a Topic Collection of Energies on “Artificial Intelligence and Smart Energy”.

The increase in human growth, alongside a higher standard of living, encourages the community to engage in progressively more activities. This is evident in the massive energy demand. Unfortunately, the current supply does not adequately meet the demands due to some challenges, including costs, techniques, technologies, resources, human skills, etc. To solve these challenges, certain approaches are utilized. However, the traditional practices, which require more resources such as equipment, labor sources, procedures, etc., are tedious and time-consuming. Presently, times are shifting towards the era of digitalization, where all aspects of life are directed towards being fast, effective, and efficient with the assistance of computers.

Artificial intelligence (AI) offers a smart way to help society achieve goals in a modern manner by implementing techniques involving predictive analytics, claims analytics, emerging issues detection, survey analysis, etc. AI covers a wide range, but the fields were not formally founded until 1956, at a conference at Dartmouth College, in Hanover.

On account of the drastic progress in intelligent energy systems, the AI and Smart Energy Topic Collection aims to provide a platform for showcasing the front-line research at the crossing point between AI applications, smart approaches, and energy systems. This Topic Collection also provides the latest research progress in the multidisciplinary approach to AI in energy systems, technology, development, etc. This Topic Collection considers full-length articles, short communications, perspectives, and review articles. Focal points of the AI and Smart Energy Topic Collection include but are not limited to: 

  • Energy topics:
    • Solar thermal energy;
    • Hydropower;
    • Geothermal power;
    • Wind power;
    • Marine energy;
    • Biomass and bioenergy;
    • Hydrogen energy;
    • Nuclear energy;
    • Fossil and green fuels;
    • Energy storage and saving;
    • Energy management;
    • Smart grids;
    • Energy sustainability;
    • Energy modeling.
  • Statistical approaches:
    • Taguchi method;
    • Response surface methodology;
    • Analysis of variance;
    • Linear regression;
    • Others.
  • Artificial intelligence and evolutionary computation:
    • Genetic algorithm;
    • Particle swarm optimization;
    • Nelder–Mead algorithm;
    • Multi-objective genetic algorithm;
    • Others.
  • Data mining and analysis:
    • Neural network;
    • Convolutional neural network;
    • Multivariate adaptive regression splines;
    • Decision tree;
    • K-means clustering;
    • Others.

Prof. Dr. Wei-Hsin Chen
Prof. Dr. Núria Agell
Dr. Zhiyong Liu
Prof. Dr. Ying-Yi Hong
Collection 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 papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection 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 2000 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
  • smart energy
  • intelligent energy system
  • smart approaches
  • AI technology
  • AI development
  • statistical approach
  • evolutionary computation
  • data mining
  • data analysis

Published Papers (1 paper)

2021

Article
Dynamic Uncertain Causality Graph Applied to the Intelligent Evaluation of a Shale-Gas Sweet Spot
Energies 2021, 14(17), 5228; https://doi.org/10.3390/en14175228 - 24 Aug 2021
Viewed by 191
Abstract
Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, [...] Read more.
Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, a dynamic uncertainty causality graph (DUCG) is applied for the first time to shale-gas sweet-spot evaluation. A graphical modeling scheme is presented to reduce the difficulty in model construction. The evaluation model is based on expert knowledge and does not depend on data. Through rigorous and efficient reasoning, it guarantees exact and efficient diagnostic reasoning in the case of incomplete information. Multiple conditional events and weighted graphs are proposed for specific problems in shale-gas sweet-spot evaluation, which is an extension of the DUCG that defines only one conditional event for different weighted function events and relies only on the experience of a single expert. These solutions make the reasoning process and results more objective, credible, and interpretable. The model is verified with both complete data and incomplete data. The results show that compared with other methods, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising auxiliary tool for shale-gas sweet spot evaluation. Full article
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