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Application of Information Technology (IT) for Sustainability
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Application of Information Technology (IT) for Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 5121

Special Issue Editors

Dr. Derya Birant

E-Mail Website
Guest Editor
Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey
Interests: information technology; artificial intelligence; machine learning; data mining
Dr. Zerrin Isik

E-Mail Website
Guest Editor
Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey
Interests: bioinformatics; pattern recognition; biomedical image processing; information technology

Special Issue Information

Dear Colleagues,

The application of information technology (IT) plays an important role in providing sustainability. Increasing efforts are being made to develop computer-assisted solutions to better solve a variety of sustainability-related problems, such as monitoring air quality, enhancing sustainability in health systems, advancing teaching/learning in education, reducing energy consumption, managing water, improving the performance of transportation, and increasing productivity in agriculture. However, further research into the ways in which computer technology can effectively manage, improve, or measure sustainability is necessary.

New information and communication technologies (i.e., sensors, Internet of Things (IoT), 5G, and cloud computing) allow for managing environmental, economic, and social aspects, and reaching the required sustainability levels. For example, the use of data mining and machine learning techniques to establish a sustainability framework is an important research topic.

This Special Issue aims to present a collection of articles on the analysis, solutions, and applications of IT for sustainability. We hope to create a platform where researchers can share their experiences, techniques, results, analyses, surveys, and debates on the application of technologies in achieving sustainability.

Dr. Derya Birant
Dr. Zerrin Isik
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. Sustainability 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 2400 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

  • information systems
  • machine learning
  • artificial intelligence
  • data mining
  • Internet of Things (IoT)
  • cloud computing
  • Big Data
  • image processing
  • optimization
  • decision support systems
  • monitoring systems
  • air pollution and climate change
  • environmental sustainability
  • sustainable medicine
  • sustainable healthcare and applications
  • energy
  • sustainability in education
  • smart transportation
  • IT in sustainable business

Published Papers (3 papers)

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Research

19 pages, 3296 KiB  
Article
Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective
by Fatih Soygazi
Sustainability 2023, 15(23), 16291; https://doi.org/10.3390/su152316291 - 24 Nov 2023
Viewed by 618
Abstract
Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are [...] Read more.
Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students. Full article
(This article belongs to the Special Issue Application of Information Technology (IT) for Sustainability)
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30 pages, 4703 KiB  
Article
Prioritization of Off-Grid Hybrid Renewable Energy Systems for Residential Communities in China Considering Public Participation with Basic Uncertain Linguistic Information
by Limei Liu, Xinyun Chen, Yi Yang, Junfeng Yang and Jie Chen
Sustainability 2023, 15(11), 8454; https://doi.org/10.3390/su15118454 - 23 May 2023
Cited by 1 | Viewed by 1000
Abstract
In recent years, the adoption of Hybrid Renewable Energy Systems (HRESs) is rapidly increasing globally due to their economic and environmental benefits. In order to ensure the smooth implementation of HRESs, it is important to systematically capture societal preferences. However, few studies focus [...] Read more.
In recent years, the adoption of Hybrid Renewable Energy Systems (HRESs) is rapidly increasing globally due to their economic and environmental benefits. In order to ensure the smooth implementation of HRESs, it is important to systematically capture societal preferences. However, few studies focus on the effective integration of public opinion into energy planning decisions. In this study, a decision-making approach for public participation in HRES planning is proposed to optimize the configuration of off-grid HRESs. First, an HRES evaluation index system considering public participation was constructed; to address the situation where the public from different backgrounds may have limited and inconsistent understanding of indicators, the basic uncertain linguistic information (BULI) is introduced to express evaluations and associated reliability levels. The indicator weights were then determined through the evaluation of both the public and the expert opinions. Second, the BULI-EDAS decision approach was developed by extending the EDAS method to the BULI environment to optimize HRES planning. Finally, the proposed model was applied to identify the optimal configuration in rural China. The comparative analysis results show that the proposed method can avoid misunderstandings and facilitate realistic public judgments. The selected optimal plan has a standardized energy price of 0.126 USD/kWh and generates 45,305 kg CO2/year, resulting in the best overall performance. The proposed HRES planning method provides a practical approach for decision makers to conduct HRES planning in a public participation environment to promote clean energy transitions. Full article
(This article belongs to the Special Issue Application of Information Technology (IT) for Sustainability)
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24 pages, 4274 KiB  
Article
Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars
by Goksu Tuysuzoglu, Kokten Ulas Birant and Derya Birant
Sustainability 2023, 15(7), 5889; https://doi.org/10.3390/su15075889 - 28 Mar 2023
Viewed by 2709
Abstract
Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction [...] Read more.
Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critical for environmentally sustainable development. In this study, an ensemble of K-stars (EK-stars) approach was proposed to predict the next-day rainfall status using meteorological data, such as the temperature, humidity, pressure, and sunshine, that were collected between the years 2007 and 2017 in Australia. This study also introduced the probability-based aggregating (pagging) approach when building and combining multiple classifiers for rainfall prediction. In the implementation of the EK-stars, different experimental setups were carried out, including the change of input parameter of the algorithm, the use of different methods in the pagging step, and whether the feature selection was performed or not. The EK-stars outperformed the original K-star algorithm and the recently proposed studies in terms of the classification accuracy by making predictions that were the closest to reality. This study shows that the proposed method is promising for generating accurate predictions for the sustainable development of environmental systems. Full article
(This article belongs to the Special Issue Application of Information Technology (IT) for Sustainability)
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