Renewable Energy Location in Disruption Situation by MCDM Method and Machine Learning
Publish place: International journal of industrial engineering and operational research، Vol: 5، Issue: 4
Publish Year: 1402
Type: Journal paper
Language: English
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Document National Code:
JR_BGS-5-4_007
Index date: 5 February 2024
Renewable Energy Location in Disruption Situation by MCDM Method and Machine Learning abstract
In times of disruption and uncertainties, identifying suitable locations for renewable energy projects becomes crucial. This paper explores the use of Multi-Criteria Decision-Making (MCDM) methods to determine optimal locations for renewable energy installations. The study aims to address challenges faced during disruption situations and provide insights into decision-making processes for renewable energy investments. A comprehensive review of the literature is conducted, followed by the application of MCDM techniques to evaluate potential locations. Numerical results demonstrate the effectiveness of the proposed approach, highlighting the importance of considering multiple criteria when making decisions related to renewable energy projects. The findings have implications for policymakers, investors, and stakeholders involved in the renewable energy sector.In times of disruption and uncertainties, identifying suitable locations for renewable energy projects becomes crucial. This paper explores the use of Multi-Criteria Decision-Making (MCDM) methods to determine optimal locations for renewable energy installations. The study aims to address challenges faced during disruption situations and provide insights into decision-making processes for renewable energy investments. A comprehensive review of the literature is conducted, followed by the application of MCDM techniques to evaluate potential locations. Numerical results demonstrate the effectiveness of the proposed approach, highlighting the importance of considering multiple criteria when making decisions related to renewable energy projects. The findings have implications for policymakers, investors, and stakeholders involved in the renewable energy sector.
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Renewable Energy Location in Disruption Situation by MCDM Method and Machine Learning authors
Seyedkian Rezvanjou
Department of Engineering, California State University East Bay, Hayward, California, ۹۴۵۴۲
Mahyar Amini
Department of Industrial Engineering, Islamic Azad University, Tehran, Iran
Mohammad Bigham
Department of Civil Engineering, University of Houston, Houston, Texas, USA