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Energies
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Artificial Intelligence and Data Mining in Energy and Environment
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Artificial Intelligence and Data Mining in Energy and Environment

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 (11 July 2023) | Viewed by 4591

Special Issue Editors

Dr. Sohrab Zendehboudi

E-Mail Website
Guest Editor
Department of Process Engineering, Memorial University, St. John’s, NL A1C 5S7, Canada
Interests: energy and environment; transport phenomena; carbon capture, utilization, and sequestration; multiscale and multi-physics modelling; process systems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Yankai Cao

E-Mail Website
Guest Editor
Assistant Professor, Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
Interests: large-scale optimization; machine learning; energy systems; process control

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) methods have recently gained increasing attention from scholars and engineers in different fields, these powerful methods displaying great importance in the study of various processes (and phenomena) in science and engineering. The energy and environmental processes generally suffer from various uncertainties and complexities demanding conventional and hybrid connectionist tools to be used for a variety of purposes, including the classification, clustering, simulation and modeling, process development, control, identification, monitoring, optimization, and prediction upon data availability. In addition, there might be a lack of enough knowledge regarding governing phenomena and mechanisms, as well as the non-linearity and high dimensionality related to corresponding processes. Smart modeling (e.g., AI) tools are able to offer effective practical solutions to such complex processes and scenarios if real data and modeling results are available. The aim of this Special Issue is to publish research and review papers addressing the important theoretical and practical aspects of AI and ML tools in energy and the environment, highlighting the data mining and analytics in sustainable energy production and the utilization and environmental remediation. We welcome the submission of both applied and fundamental studies in AI and ML, and articles selected for this Special Issue on “Artificial Intelligence and Data Mining in Energy and Environment” will be subject to a peer-review procedure, with the aim of a rapid and wide dissemination of research results.

Dr. Sohrab Zendehboudi
Dr. Yankai Cao
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
  • model selection
  • sustainable energy
  • environmental remediation
  • data management
  • statistical analysis

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Published Papers (2 papers)

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Research

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17 pages, 6474 KiB  
Article
Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU
by Zhuoqun Zou, Jing Wang, Ning E, Can Zhang, Zhaocai Wang and Enyu Jiang
Energies 2023, 16(18), 6625; https://doi.org/10.3390/en16186625 - 14 Sep 2023
Cited by 5 | Viewed by 1405
Abstract
Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties [...] Read more.
Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Mining in Energy and Environment)
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Review

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14 pages, 548 KiB  
Review
Review of Urban Drinking Water Contamination Source Identification Methods
by Jinyu Gong, Xing Guo, Xuesong Yan and Chengyu Hu
Energies 2023, 16(2), 705; https://doi.org/10.3390/en16020705 - 7 Jan 2023
Cited by 13 | Viewed by 2465
Abstract
When drinking water flows into the water distribution network from a reservoir, it is exposed to the risk of accidental or deliberate contamination. Serious drinking water pollution events can endanger public health, bring about economic losses, and be detrimental to social stability. Therefore, [...] Read more.
When drinking water flows into the water distribution network from a reservoir, it is exposed to the risk of accidental or deliberate contamination. Serious drinking water pollution events can endanger public health, bring about economic losses, and be detrimental to social stability. Therefore, it is obviously crucial to research the water contamination source identification problem, for which scholars have made considerable efforts and achieved many advances. This paper provides a comprehensive review of this problem. Firstly, some basic theoretical knowledge of the problem is introduced, including the water distribution network, sensor system, and simulation model. Then, this paper puts forward a new classification method to classify water contamination source identification methods into three categories according to the algorithms or methods used: solutions with traditional methods, heuristic methods, and machine learning methods. This paper focuses on the new approaches proposed in the past 5 years and summarizes their main work and technical challenges. Lastly, this paper suggests the future development directions of this problem. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Mining in Energy and Environment)
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