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Home / Journals Science Journal of Energy Engineering / Soft Computing Techniques for Energy Engineering
Soft Computing Techniques for Energy Engineering
Submission Deadline: May 30, 2015
Lead Guest Editor
School of Economics and Management, North China Electric Power University, Beijing, China
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Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
Authors: Huiru Zhao, Sen Guo
Pages: 14-21 Published Online: Feb. 10, 2015
Views 3991 Downloads 267
Authors: Sen Guo, Huiru Zhao
Pages: 6-13 Published Online: Dec. 27, 2014
Views 3446 Downloads 197
Authors: Yanlin Qu, Yilan Su
Pages: 1-5 Published Online: Dec. 27, 2014
Views 3696 Downloads 273
Soft Computing is a term used in computer science to refer to problems in computer science whose solutions are unpredictable, uncertain. In the past few years, soft computing techniques have obtained great development, which have been applied in many fields, such as engineering science, social science, and so on. In practice, many energy engineering issues face with the uncertain problems. The soft computing techniques can be employed to solve the unpredictable and uncertain issues of energy engineering to some extent. Therefore, it is very meaning, practical and promising topic that employs the soft computing techniques on energy engineering problems.

This special issue intends to provide the details of recent advances of soft computing techniques and promote the applications of soft computing techniques in the energy engineering context.

Potential topics include, but are not limited to:

(1) Recent computational intelligence methods for energy engineering, such as neural networks and SVM (support vector machine) for energy forecasting;
(2) Bio-inspired optimization algorithms for energy engineering, such as ant colony optimization algorithm (ACO), Fruit Fly Optimization Algorithm (FOA) for energy optimization;
(3) Multiple criteria decision-making (MCDM) for energy engineering, such as TOPSIS, AHP/ANP, fuzzy comprehensive evaluation method for energy issues.
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