Special Issue "Human-Technology Interaction Sustainable Data Use for Environmental Decision Making"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Yuchul Jung
E-Mail Website
Guest Editor
Department of Computer Engineering, Kumoh National Institute of Technology (KIT), Gumi 39177, Korea
Interests: information retrieval and natural language processing; SW-based robotics; public health; artificial intelligence (AI); machine learning stuffs
Special Issues and Collections in MDPI journals
Prof. Dr. Dongjun Suh
E-Mail Website
Guest Editor
Department of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea
Interests: data science; AI; machine learning; smart control; energy ICT
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Human–technology interaction (HTI) is an interdisciplinary research area that focuses on the development of products for human–environment interaction. HTI refers to the interaction between humans and technology (i.e., hardware and software with any technology). In addition, it encompasses the processes, actions and dialogues that a user engages in to interact with technology. In this context, we expect that the technological advancements will enable more comprehensive, sustainable data and real-time information for improved decision making and collaborations.

This Special Issue solicits high-quality scientific contributions on sustainable data use for environmental decision making in terms of HTI. We encourage submissions of research contributions that advance our theoretical understanding of the field of environmental decision making, report experimental investigations of decision-making mechanisms in various types of environments, propose innovative solutions to the design of environmental decision-making systems, or provide novel perspectives on various types of environments or technological advancements of interest across scientific boundaries. Within this framework, authors are invited to submit manuscripts for consideration to be published in this Special Issue addressing, but not limited to, the following:

  • Processes of environmental decision making through human–technology interaction;
  • Sustainable data use in various types of environmental decision making for a sustainable environment, such as the smart environment, smart industry, smart city, and sustainable energy domains;
  • Software frameworks, experiments, and case studies in the context of data-driven decision making;
  • Machine learning (including deep learning) approaches for advanced environmental decision making;
  • Collective interactions involving environmental decision making;
  • Alternative concepts, theories and methods that reflect environmental decision contexts for cyber and physical environments.

Prof. Dr. Yuchul Jung
Prof. Dr. Dongjun Suh
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 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 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 1900 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

  • sustainable data use in various contexts
  • decision making with data
  • decision support system
  • knowledge management for various types of environments
  • predictive model
  • AI/ML-based decision making
  • artificial intelligence
  • machine learning
  • deep learning
  • big data analytics
  • decision making-based applications

Published Papers (1 paper)

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Research

Article
Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building
Sustainability 2021, 13(15), 8660; https://doi.org/10.3390/su13158660 - 03 Aug 2021
Viewed by 366
Abstract
Renewable energy systems are an alternative to existing systems to achieve energy savings and carbon dioxide emission reduction. Subsequently, preventing the reckless installation of renewable energy systems and formulating appropriate energy policies, including sales strategies, is critical. Thus, this study aimed to achieve [...] Read more.
Renewable energy systems are an alternative to existing systems to achieve energy savings and carbon dioxide emission reduction. Subsequently, preventing the reckless installation of renewable energy systems and formulating appropriate energy policies, including sales strategies, is critical. Thus, this study aimed to achieve energy reduction through optimal selection of the capacity and lifetime of solar thermal (ST) and ground source heat pump (GSHP) systems that can reduce the thermal energy of buildings including the most widely used photovoltaic (PV) systems. Additionally, this study explored decision-making for optimal PV, ST, and GSHP installation considering economic and environmental factors such as energy sales strategy and electricity price according to energy policies. Therefore, an optimization model based on multi-objective particle swarm optimization was proposed to maximize lifecycle cost and energy savings based on the target energy savings according to PV capacity. Furthermore, the proposed model was verified through a case study on campus buildings in Korea: PV 60 kW and ST 32 m2 GSHP10 kW with a lifetime of 50 years were found to be the optimal combination and capacity. The proposed model guarantees economic optimization, is scalable, and can be used as a decision-making model to install renewable energy systems in buildings worldwide. Full article
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