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This paper proposes an extension of fuzzy multi-criteria decision making (MCDM) approach for selecting solar PV energy technologies. In the proposed approach, several PV technologies are used as the alternatives. The ratings of alternatives - PV technologies under various criteria and the weights of criteria are assessed in linguistic terms represented by fuzzy numbers. | 796 Moving Integrated Product Development to Service Clouds in the Global Economy J. Cha et al. (Eds.) © 2014 The Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License. doi:10.3233/978-1-61499-440-4-796 Selecting Renewable Energy Technology via a Fuzzy MCDM Approach Luu Quoc DATa,1, Shuo-Yan CHOUb, Nguyen Truc LEa, Evina WIGUNAb, Tiffany Hui-Kuang YUc, Phan Nguyen Ky PHUCb a University of Economics and Business, Vietnam National University, 144 Xuan Thuy Rd., Hanoi, Viet Nam b Department of Industrial Management, National Taiwan University of Science and Technology, 43, Section 4, Keelung Rd., Taipei 10607, Taiwan, ROC c Department of Public Finance, Feng Chia University, 100 Wenhwa Rd., Taichung, 407 Taiwan, ROC Abstract. Renewable energy technology selection, which has a strategic importance for many countries and companies, is one of the most challenging decisions due to the complex features and large number of alternatives. Of all renewable energy sources, solar photovoltaic (PV) energy has attracted more attention as the greatest promising option for industrial application. This paper proposes an extension of fuzzy multi-criteria decision making (MCDM) approach for selecting solar PV energy technologies. In the proposed approach, several PV technologies are used as the alternatives. The ratings of alternatives - PV technologies under various criteria and the weights of criteria are assessed in linguistic terms represented by fuzzy numbers. These values are further averaged and normalized into a comparable scale. Then, the normalized weighted rating can be derived by interval arithmetic of fuzzy numbers. To avoid complicated aggregation of fuzzy numbers, these normalized weighted ratings are defuzzified into crisp values using the left and right indices ranking approach. Finally, this study applies the proposed fuzzy MCDM approach to .
