Data-Driven Insights and Practices in Sustainable Development
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (14 June 2024) | Viewed by 2123
Special Issue Editor
Interests: renewable energy sources; wind power; wide area backup protection; WAMS and nonlinear complex system theory
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In this era of big data, data science plays a pivotal role in sustainable development. It enables individuals, businesses, and social organizations to optimize resource utilization, enhance data application efficiency, and improve research outcomes. By integrating data management, computation, statistics, visualization, and domain knowledge, data science accurately captures the complexity of phenomena and supports data-driven decision-making. Through predictive modeling, pattern recognition, and optimization, data science enhances research results and uncovers anomalies and associations that traditional methods may overlook. It also fosters interdisciplinary practices and holistic research approaches, fostering progress and collaboration across various fields. By effectively harnessing resources and improving methodologies, data science reveals insights with significant potential and benefits, benefiting individuals, businesses, and social organizations. Data science is not only an important research tool but also a crucial paradigm that guides research methods and cognition, providing support and guidance for sustainable development.
This Special Issue is dedicated to providing a platform for the exchange of information on the application of data science in important fields, and will include a series of topical articles covering the latest techniques, tools, and methods in data science, as well as the sharing of success stories and practical experiences. This Special Issue welcomes original research articles and reviews that discuss the latest research on the theory, methods, techniques, and applications of data science in various fields. Research areas may include (but are not limited to) the following: smart grid operation optimization in the context of carbon emission reduction, big data and power systems, big data and kinesiology, engineering equipment diagnosis and maintenance, supply chain management, and other related topics.
Thank you for your support! I look forward to receiving your contributions.
Prof. Dr. Yagang Zhang
Guest Editor
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
- renewable energy sources
- wind power
- wide area backup protection
- WAMS and nonlinear complex system theory
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