Integer Code Series Enhanced IT2 Fuzzy Decision Support System With Alpha Cuts for the Innovation Adoption Life Cycle Pattern Recognition of Renewable Energy Alternatives

This study aims to evaluate the innovation adoption performance of the renewable energy alternatives. Within this context, a new model is created that consists of two different stages. Firstly, the decision combinations of innovation adoption life cycle patterns are identified by considering integer code series. On the other side, in the second stage, the innovation adoption life cycle performance of the renewable energy alternatives is ranked. In this framework, interval type-2 (IT2) fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methodology is taken into consideration based on alpha cut levels. Moreover, a comparative evaluation is also conducted by considering IT2 fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). It is identified that the analysis results are quite coherent and reliable. The findings demonstrate that solar energy is the most appropriate renewable energy type to make innovation. Additionally, wind and geothermal energies are also other significant renewable energy types that have a great innovation potential. However, it is also concluded that biomass and hydroelectric energy have lower importance in comparison with the others. Another important point is that the ranking results are quite similar for different alpha cuts. This condition indicates that the analysis results are coherent and reliable. It is obvious that making investment to solar energy alternatives provides opportunity to adopt technologic innovation more easily. Therefore, it is recommended that investors should give priority to solar energy projects so that it can be more possible to increase the efficiency and the profitability of the investments.

[1]  Bhim Singh,et al.  A framework for comparative evaluation of lean performance of firms using fuzzy TOPSIS , 2013 .

[2]  Zheng-Xia He,et al.  Factors That Influence Renewable Energy Technological Innovation in China: A Dynamic Panel Approach , 2018 .

[3]  Babak Daneshvar Rouyendegh,et al.  Using Intuitionistic Fuzzy TOPSIS in Site Selection of Wind Power Plants in Turkey , 2018, Adv. Fuzzy Syst..

[4]  Jianming Zhan,et al.  Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: An application to biomedical problems , 2020, Inf. Sci..

[5]  Kun Wang,et al.  The development technology and applications of supercritical CO2 power cycle in nuclear energy, solar energy and other energy industries , 2017 .

[6]  Vahan Gevorgian,et al.  Data-driven Optimal Control Strategy for Virtual Synchronous Generator via Deep Reinforcement Learning Approach , 2021 .

[7]  Karoline S. Rogge,et al.  Exploring the Role of Instrument Design and Instrument Interaction for Eco-Innovation: A Survey-Based Analysis of Renewable Energy Innovation in Germany , 2018 .

[8]  Ömer Alkan,et al.  Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA , 2020 .

[9]  Ki-Hyun Kim,et al.  Solar energy: Potential and future prospects , 2018 .

[10]  Johannes Urpelainen,et al.  Global patterns of renewable energy innovation, 1990–2009 , 2013 .

[11]  J. Watson,et al.  Renewable energy production will exacerbate mining threats to biodiversity , 2020, Nature Communications.

[12]  Joëlle Noailly,et al.  Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data , 2015 .

[13]  Krishna Reddy Chittedi,et al.  The effect of renewable energy consumption on economic growth: Evidence from the renewable energy country attractive index , 2020, Energy.

[14]  I. Lopez-Yanez,et al.  Using Binary Decision Diagrams to Efficiently Represent Alpha-Beta Associative Memories , 2006, Electronics, Robotics and Automotive Mechanics Conference (CERMA'06).

[15]  Fang Liu,et al.  Trapezoidal Interval Type-2 Fuzzy TOPSIS Using Alpha-Cuts , 2020, International Journal of Fuzzy Systems.

[16]  Edgardo Manuel Felipe Riverón,et al.  A Novel Approach to Automatic Color Matching , 2006, CIARP.

[17]  Huchang Liao,et al.  A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India , 2019, Journal of Cleaner Production.

[18]  Serhat Yüksel,et al.  An integrated stochastic fuzzy MCDM approach to the balanced scorecard-based service evaluation , 2019, Math. Comput. Simul..

[19]  Yongping Sun,et al.  Can CDM projects trigger host countries’ innovation in renewable energy? Evidence of firm-level dataset from China , 2020 .

[20]  Hasan Dinçer,et al.  Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets , 2020 .

[21]  Deger Saygin,et al.  The Implications for Renewable Energy Innovation of Doubling the Share of Renewables in the Global Energy Mix between 2010 and 2030 , 2015 .

[22]  Wen‐Chang Chen,et al.  Biaxially-extended side-chain engineering of benzodithiophene-based conjugated polymers and their applications in polymer solar cells , 2020 .

[23]  Francesco Vona,et al.  Environmental policies, competition and innovation in renewable energy , 2014 .

[24]  Felix Groba,et al.  Chinese Renewable Energy Technology Exports: The Role of Policy, Innovation and Markets , 2013 .

[25]  Daniel Scholten,et al.  Renewable energy and geopolitics: A review , 2020 .

[26]  Etienne Menard,et al.  Innovative Cleaning Technique for PV Modules on Helioslite Biaxial Solar Trackers , 2018, 2018 6th International Renewable and Sustainable Energy Conference (IRSEC).

[27]  Hasan Dinçer,et al.  Analyzing the Investments Strategies for Renewable Energies Based on Multi-Criteria Decision Model , 2020, IEEE Access.

[28]  Babak Daneshvar Rouyendegh,et al.  Intuitionistic Fuzzy TOPSIS method for green supplier selection problem , 2020, Soft Comput..

[29]  Ting-Yu Chen,et al.  Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis , 2018, Inf. Fusion.

[30]  Qiuye Sun,et al.  SoC-Based Droop Coefficients Stability Region Analysis of the Battery for Stand-Alone Supply Systems With Constant Power Loads , 2021, IEEE Transactions on Power Electronics.

[31]  Hasan Dinçer,et al.  A Hybrid Hesitant 2-Tuple IVSF Decision Making Approach to Analyze PERT-Based Critical Paths of New Service Development Process for Renewable Energy Investment Projects , 2021, IEEE Access.

[32]  A. Pitelis.  Industrial policy for renewable energy: The innovation impact of European policy instruments and their interactions , 2018 .

[33]  Serhat Yüksel,et al.  Analyzing TRIZ-based strategic priorities of customer expectations for renewable energy investments with interval type-2 fuzzy modeling , 2021 .

[34]  Gita Surie.  Creating the innovation ecosystem for renewable energy via social entrepreneurship: Insights from India , 2017 .

[35]  X. Yao,et al.  Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity , 2021 .

[36]  Wei Yang,et al.  Hesitant interval‐valued Pythagorean fuzzy VIKOR method , 2019, Int. J. Intell. Syst..

[37]  David Wenzhong Gao,et al.  A Distributed Double-Newton Descent Algorithm for Cooperative Energy Management of Multiple Energy Bodies in Energy Internet , 2020, IEEE Transactions on Industrial Informatics.

[38]  H. Di̇nçer,et al.  IT2 Fuzzy-Based Multidimensional Evaluation of Coal Energy for Sustainable Economic Development , 2020, Energies.

[39]  H. Di̇nçer,et al.  Multidimensional evaluation of global investments on the renewable energy with the integrated fuzzy decision‐making model under the hesitancy , 2019, International Journal of Energy Research.

[40]  K. Chalvatzis,et al.  Fostering innovation in renewable energy technologies: Choice of policy instruments and effectiveness , 2020, Renewable Energy.

[41]  Ying Wang,et al.  Strategic renewable energy resources selection for Pakistan: Based on SWOT-Fuzzy AHP approach , 2020 .

[42]  M. D. Fonseca,et al.  Working towards a framework based on mission-oriented practices for assessing renewable energy innovation policies , 2018, Journal of Cleaner Production.

[43]  Oscar Castillo,et al.  Finite-interval-valued Type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem , 2020, Eng. Appl. Artif. Intell..

[44]  Ali Fuat Guneri,et al.  Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry. , 2019, Journal of safety research.

[45]  Cornelio Yáñez-Márquez,et al.  Classifying Patterns in Bioinformatics Databases by Using Alpha-Beta Associative Memories , 2009, Biomedical Data and Applications.

[46]  Muhammad Akram,et al.  Bipolar fuzzy TOPSIS and bipolar fuzzy ELECTRE-I methods to diagnosis , 2019, Computational and Applied Mathematics.

[47]  Juliana Subtil Lacerda,et al.  Effectiveness of an ‘open innovation’ approach in renewable energy: Empirical evidence from a survey on solar and wind power , 2020 .

[48]  N. Hilal,et al.  Solar powered desalination – Technology, energy and future outlook , 2019, Desalination.

[49]  Arunodaya Raj Mishra,et al.  A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection , 2020, Journal of Cleaner Production.

[50]  Hasan Dinçer,et al.  Comparative Evaluation of BSC-Based New Service Development Competencies in Turkish Banking Sector with the Integrated Fuzzy Hybrid MCDM Using Content Analysis , 2018, International Journal of Fuzzy Systems.

[51]  Hasan Dinçer,et al.  Kano-based mapping of innovation strategies for renewable energy alternatives using hybrid interval type-2 fuzzy decision-making approach , 2020 .

[52]  Daniel J. Fiorino,et al.  The Implications of Policy Stability for Renewable Energy Innovation in the United States, 1974–2009 , 2013 .

[53]  Abbas Mardani,et al.  Investigating the effects of renewable energy on international trade and environmental quality. , 2020, Journal of environmental management.

[54]  Yo. K. Toshmurodov,et al.  Method for Increasing the Efficiency of a Biaxial Solar Tracker with Exact Solar Orientation , 2018 .

[55]  Campos Inês,et al.  Regulatory challenges and opportunities for collective renewable energy prosumers in the EU , 2020 .

[56]  Tzung-Pei Hong,et al.  The invention of new sequences through classifying and counting fuzzy matrices , 2020, Soft Computing.