Evaluation of Renewable Energy Resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations

Büyüközkan, Gülçin; Güleryüz, Sezin

doi:10.1016/j.energy.2017.01.137

Cited by 148 publications

(67 citation statements)

References 53 publications

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“…After the calculation of the total influence matrix, the next step is to convert these matrices into the posterior matrix utilizing Equations (24) to (26). The posterior matrix is derived as shown below:…”

Section: Br-dematelmentioning

confidence: 99%

“…The fuzzy and grey theories are representative examples. The incorporation of these two theories into the MCDM model has been used to solve a great number of issues, such as project selection [22], performance evaluation [23], evaluation of renewable energy resources [24], and selection of sustainable recycling partners [25]. Rough interval numbers, which recently have received much more attention, is another effective approach that can be utilized to deal with imprecise numeric values in decision data and subjectively collective judgments without defining membership function.…”

Section: Introductionmentioning

confidence: 99%

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Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

2019

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Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.

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Section: Br-dematelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

2019

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“…Type-2 FSs can be considered as an extension of type-1 FSs. Because type-2 interval FSs are used instead of traditional type-1 FSs in order to represent weights of the qualities and evaluation values, type-2 FSs provide us a more beneficial method for the solution of the fuzzy multicriteria decision-making problems in a more flexible and intelligent way [20][21][22][23][24].…”

Section: Type-2 Fuzzy Setsmentioning

confidence: 99%

A Multi-Criteria Decision-Making Methodology Suggestion for Turkey Energy Planning Based Type-2 Fuzzy Sets

Balın¹,

Baraçlı²

2018

Decision Making

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Energy as an essential basis for the social development has a vital role for survival and development of humankind as an environmental factor. Energy consumption of Turkey has become an important problem through the exorbitant price increase in the fundamental energy source of the world and rapid development in the economy of Turkey. The necessity to create correct decision-making processes related to future in order to eliminate this problem has appeared as well. For that reason, views of decision-makers upon the relative importance of selection criteria were determined, using analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) based upon type-2 fuzzy sets (FSs) that were used in order to list the best energy alternatives.

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“…Niet et al implemented a stochastic risk structure into the open source energy modeling system optimization model to incorporate uncertainty related to the emissions of electricity generation technologies [22]. Büyüközkan and Güleryüz established an evaluation model to select the most appropriate renewable energy resources in Turkey [23]. Osuna-Gómez et al solved optimization problems where both the objective and constraints are given by fuzzy functions [24].…”

Section: Introductionmentioning

confidence: 99%

Integrated Planning for Regional Electric Power System Management with Risk Measure and Carbon Emission Constraints: A Case Study of the Xinjiang Uygur Autonomous Region, China

Xie

et al. 2019

Energies

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With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk of reducing emissions and the fuzziness of carbon capture technology popularization rate and carbon reduction targets makes previous planning methods unsatisfactory for current planning. This paper establishes an interval fuzzy programming with a risk measure model which takes carbon capture technology and carbon reduction targets into account, to ensure that the complex electric management system achieves the best developmental state. It was concluded that in order to reduce carbon emissions, wind power and hydropower would be the best choices, and coal-fired power would be the suboptimal choice, and solar power would play a complementary role. Besides, decision makers should put much more effort into promoting and improving carbon capture technology instead of simply setting emission reduction targets. The non-synchronism of the downward trend in carbon emissions per unit of electricity generation and electric power industry total carbon emissions need to be taken seriously.

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Evaluation of Renewable Energy Resources in Turkey using an integrated MCDM approach with linguistic interval fuzzy preference relations

Cited by 148 publications

References 53 publications

Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

A Multi-Criteria Decision-Making Methodology Suggestion for Turkey Energy Planning Based Type-2 Fuzzy Sets

Integrated Planning for Regional Electric Power System Management with Risk Measure and Carbon Emission Constraints: A Case Study of the Xinjiang Uygur Autonomous Region, China

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