General Information
    • ISSN: 1793-821X (Print)
    • Abbreviated Title: J. Clean Energy Technol.
    • Frequency: Quarterly (2013-2014); Bimonthly (Since 2015)
    • DOI: 10.18178/JOCET
    • Editor-in-Chief: Prof. Haider F. Abdul Amir
    • Executive Editor: Ms. Jennifer Zeng
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Chemical Abstracts Services (CAS), Ulrich's Periodicals Directory, Google Scholar, ProQuest.
    • E-mail: jocet@ejournal.net
  • Aug 28, 2019 News! JOCET Vol. 7, No. 5 is available online now.   [Click]
  • Jun 19, 2019 News! JOCET Vol. 7, No. 4 is available online now.   [Click]
Editor-in-chief
Universiti Malaysia Sabah, Malaysia.
I would like to express my appreciation to all the reviewers and editors, who have been working
very hard to ensure the quality of the journal. It's my honor to work with such a wonderful team.

Journal of Clean Energy Technologies (JOCET) is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of Clean Energy Technologies. The focus is to publish papers on state-of-the-art Clean Energy Technologies. Submitted papers will be reviewed by technical committees of the Journal and Association. The audience includes researchers, managers and operators for Clean Energy Technologies as well as designers and developers.
 
All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing.

Featured Article


The Impact of Renewable Energy for Occupational Health in the Smart Grid Era
Saki Gerassis, Alberto Abad, Eduardo Giráldez, and Javier Taboada
The aim of this study is to analyze how the growth of renewable energy in the power market is affecting workers health and what are the cost implications of having a healthier workforce. To tackle this issue, Big Data from occupational health surveillance carried out to over 4,000 workers in Spanish companies is used to unveil hidden patterns and relevant factors affecting workers health. Machine learning is used to create a predictive Bayesian model in order to seek out relevant patterns ....[Read More]
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E-mail: jocet@ejournal.net