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Mathematics
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Artificial Intelligence in Geoenvironmental and Energy Sciences
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Artificial Intelligence in Geoenvironmental and Energy Sciences

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 5273

Special Issue Editors

Prof. Dr. Ahmed E. Radwan

E-Mail Website
Guest Editor
Institute of Geological Sciences, Jagiellonian University, 31-007 Krakow, Poland
Interests: subsurface analysis; machine learning application in the petroleum industry; applied geophysics; petroleum geochemistry; petroleum geomechanics; petroleum and reservoir engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Hamzeh Ghorbani

E-Mail Website
Guest Editor
1. Faculty of General Medicine, University of Traditional Medicine of Armenia, Yerevan 0040, Armenia
2. Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
Interests: energy science; artificial intelligence; petroscience; computational intelligence; geoscience; medical science; cancer; oncology; optimization; polymer nanocomposite

Special Issue Information

Dear Colleagues,

Earth sciences and energy sciences are among the most important sciences that humans have been developing for many years, and they are among the most fundamental sciences for human survival and development. Understanding the interaction between environmental and energy sciences is vital for geo-environmental modeling and monitoring, energy production, environmental safety, energy efficiency, and life in general. To study the challenges that humans face and determine their dimensions in various aspects of science, a large dataset is required. Artificial intelligence, on the other hand, has undergone a major revolution in predicting, optimizing, and determining the key parameters of various sciences and is now going beyond traditional approaches, leading to safer, more efficient, and more cost-effective solutions, with the ability to accurately predict key parameters of earth sciences and extraterrestrial sciences. Breakthroughs and significant progress have been made in the last few decades in the use of scientific data for predictive analytics. This special issue aims to achieve, predict, and optimize the key parameters of earth sciences and energy sciences using machine learning and deep learning.

Prof. Dr. Ahmed E. Radwan
Dr. Hamzeh Ghorbani
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. Mathematics 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

  • deep learning
  • data driven modeling
  • innovation technology
  • prediction key factor for earth science
  • artificial intelligence
  • machine learning
  • prediction key factor for energy science
  • prediction of oil and gas key parameter
  • energy process digitization
  • environmental safety by AI
  • determination of key factors for the environment in AI
  • geoenvironmental modeling and monitoring
  • environmental robotics

Published Papers (1 paper)

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Research

19 pages, 3500 KiB  
Article
Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model
by Elham M. Al-Ali, Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. Laatar and Mohamed Atri
Mathematics 2023, 11(3), 676; https://doi.org/10.3390/math11030676 - 28 Jan 2023
Cited by 14 | Viewed by 4807
Abstract
Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very [...] Read more.
Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids. Full article
(This article belongs to the Special Issue Artificial Intelligence in Geoenvironmental and Energy Sciences)
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