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Soft-Computing for Renewable Energy System Design

Submission Deadline: 30 December 2022 (closed) View: 24

Guest Editors

Dr. Nguyen Van Thanh, Van Lang University, Vietnam.
Dr. Phan Nguyen Ky Phuc, International University, Vietnam.
Dr. Nguyen Huu Tho, Nguyen Tat Thanh University.


The Earth’s climate and environment have been experiencing drastic changes in the past few decades. As our fossil fuels resources continue to deplete and our way of life being increasingly threatened, researchers and policy makers are urgently searching for new ways to preserve our limited resources as well as to improve the current environmental conditions. The answer to these problems can be found through soft computing, which has been constantly evolving as a faster pace than ever before with new techniques and applications.

Soft computing as opposed to hard computing, work with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations. This allows soft computing to be successfully applied many sectors and industries. Soft computing offers new approaches, which are characterized by hybrid solutions and intelligent methods, to existing problems. These approaches construct effective solutions to many problems based on axiomatic mathematical theories and systems.

In the renewable energy field, soft-computing approaches can be applied independently or in conjunction with other approaches to solve managerial and technical problems such as decision support, risk management, energy efficiency evaluation, group decision making, public policies development, project development process, process design, etc.

This Special Issue on Axioms in Soft computing for Renewable Energy development aims to collect high-quality research studies addressing renewable energy related problems, focusing on the application of soft computing techniques in the Renewable Energy field. The special issue focuses on the application soft-computing classical approaches such as fuzzy logic, support vector machines, genetic algorithms, artificial neural networks, machine learning, and rough sets in the framework of optimization, multicriteria decision-making, game theory, outranking, and others.


Multi-Criteria Decision Making (MCDM)
Renewable Systems Design
Fuzzy Sets
Genetic Algorithms
Neural Network
Fuzzy Neural Network
Expert Systems
Machine Learning
Renewable Energy Project Management
Sustainable Projects

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