Modelling Solar Energy Usage with Fuzzy Cognitive Maps

Solar energy is a reliable and sustainable energy resource but its usage is still limited. Modeling solar energy generation capacity can help increasing solar energy usage. In this chapter, the factors that affect solar energy usage and the relations among them are defined by a comprehensive literature review. These factors are complex and the relations between them are ambiguous. Fuzzy cognitive maps are used for modelling solar energy generation capacity. Fuzzy cognitive maps are excellent tools for modelling such complexity and ambiguity. The relations among the factors are defined based on the experts’ opinions. Different scenarios are developed and the changes in the solar energy generation capacity have been analyzed.

[1]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[2]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[3]  Athanasios K. Tsadiras,et al.  Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps , 2008, Inf. Sci..

[4]  M. Zhang,et al.  A real option model for renewable energy policy evaluation with application to solar PV power generation in China , 2014 .

[5]  Jose L. Salmeron,et al.  Benchmarking main activation functions in fuzzy cognitive maps , 2009, Expert Syst. Appl..

[6]  Konstantinos G. Margaritis,et al.  Cognitive Mapping and Certainty Neuron Fuzzy Cognitive Maps , 1997, Inf. Sci..

[7]  Selcuk Cebi,et al.  A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process , 2009 .

[8]  J. Diffenbach Influence diagrams for complex strategic issues , 1982 .

[9]  R. Axelrod Structure of decision : the cognitive maps of political elites , 2015 .

[10]  Chuen-Chien Lee FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[11]  J. Nazuno Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[12]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

[13]  James C. Bezdek,et al.  Pool2: a generic system for cognitive map development and decision analysis , 1989, IEEE Trans. Syst. Man Cybern..

[14]  Muyiwa Adaramola Solar Energy: Application, Economics, and Public Perception , 2014 .

[15]  Bart Kosko,et al.  Virtual Worlds as Fuzzy Cognitive Maps , 1994, Presence: Teleoperators & Virtual Environments.

[16]  Michael Glykas,et al.  Fuzzy Cognitive Maps in Banking Business Process Performance Measurement , 2010 .

[17]  S. Kalogirou Chapter 12 – Solar Economic Analysis , 2014 .

[18]  Uygar Özesmi,et al.  Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach , 2004 .

[19]  Judith Gurney BP Statistical Review of World Energy , 1985 .