Interval Type-2 Fuzzy Analytic Hierarchy Process for Sustainable Energy Sources Selection

Interval Type-2 Fuzzy Analytic Hierarchy Process for Sustainable Energy Sources Selection

Lazim Abdullah, Liana Najib
Copyright: © 2017 |Pages: 14
DOI: 10.4018/IJFSA.2017070106
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Selecting sustainable energy sources is one of the efforts to ensuring optimum energy consumption. However, the selection process is not straight forward as it requires multi-criteria that inherited uncertainty and qualitative evaluation. Multi-criteria evaluation that based on fuzzy set theory is one of the well-known methods to handle uncertainty in decision making and vagueness of information. This paper develops a preference for sustainable energy using the linguistic evaluation of interval type-2 fuzzy analytic hierarchy process (IT2 FAHP). Seven alternatives of sustainable energy sources that closely related to nine criteria were identified as the hierarchical structure of the decision making problem. Two academicians and an engineer attached to government agencies were invited to provide linguistic evaluation. The linguistic evaluations were analyzed using the newly developed IT2 FAHP. The seven-step computational method was implemented and the results suggest that solar energy is the best alternative in selecting the viable sustainable energy.
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Over the past decades, growing energy demands and rising concerns over energy security and environmental effects have forced policy makers to contemplate comprehensive sustainable energy management. A good energy management would help to foresee the future of a local, national, regional and global energy system (Wang et al., 2009). Sustainable energy management normally related to long-term policy in which energy resources are used to meet mankind needs while at the same time preserving the environment. Efficient utilization of energy sources, diversification of sources and minimization of wastages are among the benefits that may gain from proper energy management. One of the most common processes in the management is selecting the most viable energy sources and technologies out of multiple numbers of energy sources. The energy sources selection problem can be conceptualized as a process of establishing optimal solution, thereby the problem could be solved using multi-criteria decision making (MCDM) methods. Previously, a number of multi-criteria decision analysis methods were developed for energy management systems and planning. The energy related issues were successfully implemented using many MCDM and fuzzy MCDM based methods, including energy optimization modelling (Dong et al., 2012), energy planning and selection (Kaya and Kahraman, 2012; Pohekar and Ramachandran, 2004; Tsoutsos et al., 2009; Wang, 2011; Abdullah and Najib, 2014a) and alternative of bioenergy systems (Scott et al., 2012). Buchholz et al., (2009) employ four multi-criteria analysis to facilitate the design and implementation of sustainable bioenergy projects. Multi-criteria decision aid was used to assess the sustainability of bioenergy systems with a special focus on multi-stakeholder inclusion using data of bioenergy case study in Uganda (Doukas et al., 2007; Jing et al., 2012). Manjili et al., (2013) proposed decision making framework based on genetic algorithms and fuzzy logic. The method was proposed to control and manage energy of micro-grids. The framework aims to meet the demand of minimizing electricity and reducing consumption cost. The framework also aims to modify air pollution under a dynamic electricity pricing policy. In another related research, Faquir, (2015) employed a decision framework based on fuzzy logic control to estimate the wind and solar energies in a hybrid renewable energy system from natural factors. An interval-fuzzy municipal-scale energy model was developed by Chen et al., (2016) for identification of optimal strategy management. The decision was made using the knowledge of intersection of membership functions corresponding to fuzzy sets in objective functions and constraints. Most of the existing fuzzy MCDM methods that have been implemented in energy management are built from linguistic terms based on type-1 fuzzy sets.

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