Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Big Data on Energy, Climate Change and Sustainability
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Big Data on Energy, Climate Change and Sustainability

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 5411

Special Issue Editors

Prof. Dr. Rameshwar Dubey

E-Mail Website
Guest Editor
Logistics and Supply Chain Management, Montpellier Business School, Montpellier, France
Interests: operations management; big data; predictive analytics
Prof. Dr. Angappa Gunasekaran

E-Mail Website
Guest Editor
School of Business and Public Administration, California State University, Bakersfield9001 Stockdale Highway, CA, USA
Interests: operations management; supply chain management; information systems; operations research; technology management
Prof. Dr. Thanos Papadopoulos

E-Mail Website
Guest Editor
Kent Business School, University of Kent, Sail and Colour Loft, The Historic DockyardChatham, Kent ME4 4TE, UK
Interests: information systems; operations management
Prof. Dr. Cyril Foropon

E-Mail Website
Guest Editor
Department of Management, Montpellier Business School, 34000 Montpellier, France
Interests: big data and predictive analytics; healthcare operations; humanitarian operations management; humanitarian supply chain management; industry 4.0; lean operations; sustainable supply chain management.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your original research or review-based works to this Special Issue on “Big Data on Energy, Climate Change and Sustainability” in Energies.

In this Special Issue, we welcome contributions within the general area integrating big data, artificial intelligence, climate change, and sustainability. The topics of interest include but are not limited to the following:

  • Big data analytics in building climate resilient supply chains;
  • Big data analytics and artificial intelligence in sustainable operations management practices;
  • Information sharing and technology adoption in sustainable operations;
  • Big data analytics in disaster relief operations resulting due to climate change;
  • Big data analytics, artificial intelligence, and social sustainability;
Prof. Dr. Rameshwar Dubey
Prof. Dr. Angappa Gunasekaran
Prof. Dr. Thanos Papadopoulos
Prof. Dr. Cyril Foropon
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. Energies 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

  • Big data analytics
  • Building climate resilient supply chains
  • Information sharing and technology adoption
  • Disaster relief operations
  • Artificial intelligence
  • Social sustainability

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 602 KiB  
Article
A Longitudinal Analysis of the Creation of Environmental Identity and Attitudes towards Energy Sustainability Using the Framework of Identity Theory and Big Data Analysis
by Dorota Domalewska
Energies 2021, 14(3), 647; https://doi.org/10.3390/en14030647 - 27 Jan 2021
Cited by 9 | Viewed by 2669
Abstract
Embracing sustainability in the 21st century entails developing environmental identity, so that attitudes towards energy sustainability result from the core values of one’s individual and social identity. This study aims to explore the shift in the formation of environmental identity and attitudes towards [...] Read more.
Embracing sustainability in the 21st century entails developing environmental identity, so that attitudes towards energy sustainability result from the core values of one’s individual and social identity. This study aims to explore the shift in the formation of environmental identity and attitudes towards energy sustainability throughout the course of the two-year study period (2018–2020). A dataset of 8,677,961 tweets, Facebook posts and comments and 325,228 news articles was collected to carry out quantitative analysis of the distribution of the posts, likes, and comments. A correlation with media coverage of energy and green topics was sought to establish the impact of the media on public debate. A qualitative analysis of posts and tweets was carried out to establish dominant themes. The findings of the study reveal that both positive attitudes towards energy sustainability and environmental identity have been consolidated throughout the two-year study period. Social media users are not only increasingly interested in green issues but also produce more reactions towards posts related to sustainability topics. The results also suggest that sustainable values and green behavior are independent from the media coverage of current events and the perceived threat to one’s health from COVID-19. Social networking sites provide a context in which users not only reinforce their beliefs and values, but also mimic the behavior of other users, which leads to the formation of a social media identity bubble that reinforces shared identity—in this case, environmental identity. This study offers a multidisciplinary perspective on sustainable development that will be able to drive equitable energy security and environmental security. Full article
(This article belongs to the Special Issue Big Data on Energy, Climate Change and Sustainability)
►▼ Show Figures

Figure 1

15 pages, 4207 KiB  
Article
Diagnosis of Blade Icing Using Multiple Intelligent Algorithms
by Xiyun Yang, Tianze Ye, Qile Wang and Zhun Tao
Energies 2020, 13(11), 2975; https://doi.org/10.3390/en13112975 - 09 Jun 2020
Cited by 12 | Viewed by 1902
Abstract
The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by [...] Read more.
The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods. Full article
(This article belongs to the Special Issue Big Data on Energy, Climate Change and Sustainability)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop