Journal of Energy and Power Technology (JEPT) is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. This periodical is dedicated to providing a unique, peer-reviewed, multi-disciplinary platform for researchers, scientists and engineers in academia, research institutions, government agencies and industry. The journal is also of interest to technology developers, planners, policy makers and technical, economic and policy advisers to present their research results and findings.

Journal of Energy and Power Technology focuses on all aspects of energy and power. It publishes not only original research and review articles, but also various other types of articles from experts in these fields, such as Communication, Opinion, Comment, Conference Report, Technical Note, Book Review, and more, to promote intuitive understanding of the state-of-the-art and technology trends.

Main research areas include (but are not limited to):
Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass) and grid connection impact
Energy harvesting devices
Energy storage
Hybrid/combined/integrated energy systems for multi-generation
Hydrogen energy 
Fuel cells
Nuclear energy
Energy economics and finance
Energy policy
Energy and environment
Energy conversion, conservation and management
Smart energy system

Power generation - Conventional and renewable
Power system management
Power transmission and distribution
Smart grid technologies
Micro- and nano-energy systems and technologies
Power electronic
Biofuels and alternatives
High voltage and pulse power
Organic and inorganic photovoltaics
Batteries and supercapacitors

Publication Speed (median values for papers published in 2023): Submission to First Decision: 5.1 weeks; Submission to Acceptance: 11.6 weeks; Acceptance to Publication: 7 days (1-2 days of FREE language polishing included)

Current Issue: 2024  Archive: 2023 2022 2021 2020 2019

Special Issue

Machine Learning and Artificial Intelligence for Power Systems

Submission Deadline: December 31, 2024 (Open) Submit Now

Guest Editors

Habib Hamam, Professor

Université de Moncton, Moncton, New Brunswick, Canada

Website | E-Mail

Research interests: Optimization; Artificial Intelligence in Engineering; Deep Learning; Intelligent Power systems; Machine Learning

Ateeq Ur Rehman, PhD

Government College University Lahore, Lahore, Pakistan

Website | E-Mail

Research interests: SioT; Data Science (ML DL AI); Big Data; Internet of Things

Dear Colleagues.

This special issue focuses on the application of artificial intelligence and machine learning models (including hybrid and integrated approaches) to power engineering prediction and optimization. Machine learning and artificial intelligence are among the most exciting areas of computing today. These methods are effective and popular in regression problems, including prediction and optimization. Efficient operation of power systems of all sizes, including microgrids, requires accurate short-term forecasts of power generation and power demand from renewable energy systems. Renewable energy generation forecasting is also important for owners of small-scale energy systems in order to optimize the use of various energy sources and to facilitate energy storage.


Artificial Intelligence/Machine Learning/Deep Learning, Renewable Energy Generation.
Power system optimization
Power System Reliability.
Advanced Energy Technologies
Power system
Smart grid technologies

Manuscript Submission Information

Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office ( for record. If you have any questions, please do not hesitate to contact the Editorial Office.

Welcome your submission!