Abstract
Alternative energy sources are gaining popularity against the world’s fossil energy sources. There is an increasing energy demand due to the growing population. Renewable energy sources are used as alter-natives for fulfilling this demand. Since these sources are non-exhaustible and can renew themselves, they are considered as primary energy sources for the future. Although solar energy has the highest capacity among renewable energy sources, currently it has the disadvantage of high equipment and installation costs. Therefore, the economic analysis of investments in solar energy systems should be accurate and realistic but the uncertainty and ambiguity inherit in the parameters make this analysis complex and inaccurate. In this work, the solar economic model containing economic and technical uncertainties has been evaluated by using fuzzy logic. Realistic solutions from the developed solar fuzzy economic model direct investors to more accurate solar power plant investments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Commission, E.: Energy statistical pocketbook (2016). http://ec.europa.eu/energy/sites/ener/files/documents/2014_pocketbook.pdf. Accessed 14 Feb 2017
Kalogirou, S.A.: Solar Energy Engineering: Processes and Systems. Academic Press, Cambridge (2013)
Resources, T.M.o.E.a.N.: Renewable energy resources support mechanism (YEKDEM) (2017). http://www.eie.gov.tr/yenilenebilir/YEKDEM.aspx. Accessed 28 Mar 2017
(EIA), U.S.E.I.A.: Renewable and Alternative Fuels (2017). https://www.eia.gov/renewable/. Accessed 28 Mar 2017
Çoban, V., Onar, S.Ç.: Modelling renewable energy usage with hesitant fuzzy cognitive map. In: Uncertainty Modelling in Knowledge Engineering and Decision Making. Proceedings of the 12th International FLINS Conference. World Scientific (2016)
Duffie, J.A., Beckman, W.A.: Solar Engineering of Thermal Processes. Wiley, Hoboken (2013)
Çoban, V., Onar, S.Ç.: Modelling solar energy usage with fuzzy cognitive maps. In: Intelligence Systems in Environmental Management: Theory and Applications, pp. 159–187. Springer, New York (2017)
Kahraman, C., et al.: Fuzzy economic analysis methods for environmental economics. In: Intelligence Systems in Environmental Management: Theory and Applications, pp. 315–346. Springer, New york (2017)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)
Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, Hoboken (2009)
Kahraman, C., Çevik Onar, S., Öztayşi, B.: Engineering economic analyses using intuitionistic and hesitant fuzzy sets. J. Intell. Fuzzy Syst. 29(3), 1151–1168 (2015)
Blank, L., Tarquin, A.: Basics of Engineering Economy. McGraw-Hill Higher Education, Pennsylvania (2013)
Kahraman, C., Öztayşi, B., Çevik Onar, S.: A comprehensive literature review of 50 years of fuzzy set theory. Int. J. Comput. Intell. Syst. 9(Suppl. 1), 3–24 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Çoban, V., Onar, S.Ç. (2018). Selection Among Solar Power Plants Using Fuzzy Economics. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-66830-7_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-66830-7_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66829-1
Online ISBN: 978-3-319-66830-7
eBook Packages: EngineeringEngineering (R0)