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Algorithms
Special Issues
Artificial Intelligence Algorithms in Sustainability
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Artificial Intelligence Algorithms in Sustainability

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2005

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. José Manuel Álvarez-Alvarado

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: solar energy; power generation; waste heat recovery; control techniques; renewable energy technologies; solar radiation; energy engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

The current technological revolution is intrinsically linked to the idea of sustainability. In this context, artificial intelligence is critical in facilitating the transition toward a more sustainable future. Within the energy field, the integration of artificial intelligence emerges as a valuable opportunity to optimize available resources and conceive new solutions for environmental improvement. Through meticulous mathematical modeling and the practical implementation of algorithms, complex problems related to resource management, energy efficiency and reducing environmental impacts can be addressed. These algorithms, based on mathematical and computational principles, make it possible to address a wide range of environmental, social and economic challenges. This convergence has led to the creation of innovative technologies, resulting in greater efficiency in various tasks in search of a sustainable society. These technological systems have found application in various industrial and research sectors, thus supporting the foundations for an energy horizon that harmoniously balances human needs with environmental conservation.

Artificial intelligence has sparked a revolution in our approach to environmental challenges. Its integration into monitoring and control systems has enabled better management of natural resources and has given rise to intelligent solutions designed with the environment in mind. These AI-backed systems can analyze data in real time and make decisions that minimize environmental impacts. In addition, these technologies prompted the development of cutting-edge forecasting and analysis techniques, enabling more accurate and effective planning for long-term sustainability. This Special Issue aims to highlight the new approaches that artificial intelligence has adopted as a cornerstone to face sustainability challenges.

We invite authors to contribute to this Special Issue. Topics of interest include but are not limited to the following:

Artificial intelligence techniques focused on sustainability engineering issues:

  • Machine learning applied to forecasting models.
  • Deep learning for image recognition for sustainability issues.
  • Optimization of autonomous systems with artificial intelligence in sustainability.
  • Metaheuristic algorithms for the optimization of power systems.
  • Diffuse or neural techniques for the analysis of sustainability data.
  • Mixed techniques for the development of intelligent systems in sustainability.

Applications with integrated artificial intelligence:

  • Photovoltaic systems.
  • Energy generation systems.
  • Environmental problems.
  • Electric vehicles.
  • Sustainable systems.
  • Alternative energy sources.
  • IoT focused on sustainability issues.

The collaboration between algorithmic research and sustainability-oriented technology unlocks significant potential to address global challenges from a scientific and technological perspective.

Prof. Dr. Juvenal Rodriguez-Resendiz
Dr. José Manuel Álvarez-Alvarado
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
  • computer vision
  • machine learning
  • deep learning
  • metaheuristics and matheuristics
  • numerical analysis
  • IoT
  • sustainability
  • green energy
  • power generation systems
  • forecasting models
  • environmental issues
  • embedded algorithms

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Published Papers (1 paper)

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20 pages, 5279 KiB  
Article
Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations
by Pavel V. Matrenin, Valeriy V. Gamaley, Alexandra I. Khalyasmaa and Alina I. Stepanova
Algorithms 2024, 17(4), 150; https://doi.org/10.3390/a17040150 - 2 Apr 2024
Cited by 5 | Viewed by 1493
Abstract
Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar [...] Read more.
Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms in Sustainability)
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