Search

This study aims to select the appropriate renewable energy alternatives for the efficiency of hybrid energy systems to increase energy transition performance. For this purpose, a novel neural network (NN)-based fuzzy decision-making model is constructed that has three different stages. In the first stage, NN-based fuzzy decision matrix is created. Secondly, 6 different variables based on industry 4.0 are weighted with the sine trigonometric Pythagorean fuzzy entropy technique. Additionally, another calculation has been implemented with criteria importance through intercriteria correlation (CRITIC) to identify the consistency of the results. Furthermore, in the third stage, considering 5 different renewable energy alternatives, 10 different combinations are identified for hybrid energy systems. The most effective alternatives are defined by the sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) method. Moreover, to test the validity of these results, another analysis is conducted using the additive ratio assessment (ARAS) technique. The main contribution of the study is that the optimal renewable energy combination required for an efficient hybrid energy system is determined by performing a priority analysis between the variables. This situation has a significant guiding feature for investors. Similarly, the development of the RATGOS technique both increases the methodological originality of the study and enables more accurate alternative ranking. It is identified that the results of all methods are similar. Therefore, this situation gives information about the coherency and validity of the findings. It is concluded that the most important criterion is real-time capability. It is also denoted that the best combination for hybrid energy systems is Solar-Wind. [ABSTRACT FROM AUTHOR]

The main performance indicators of hydrogen energy production should be improved. However, improving these factors also increase the operational costs of the companies. Because of this issue, there is a need for a priority analysis so that it can be possible to focus on more important factors. Accordingly, the purpose of this study is to evaluate hydrogen production methods for global energy transition. In this process, a four-stage model has been proposed by getting evaluations from three different experts. Firstly, artificial intelligence-based decision-making can be implemented for expert prioritization. In the second stage, recommender system is conducted with collaborative filtering to complete the missing evaluations. Thirdly, selected criteria are weighted by using M-SWARA with QPFRS. Finally, method alternatives for hydrogen production are ranked via quantum picture fuzzy rough sets adopted VIKOR. The biggest contribution for doing this study is that artificial intelligence technique is integrated into the model and experts' importance coefficients are can be computed. Additionally, by using the collaborative filtering technique, empty evaluations can be filled scientifically. This contributes to the quality of the analysis process in many ways. Thanks to this technique, experts are given the opportunity not to answer questions they are not very sure about. The findings indicate that renewable energy expansion, energy efficiency and sustainable development are the most important criteria for global energy transition in hydrogen production. On the other side, the ranking results give information that thermal processes including steam methane reforming and biomass gasification is the most appropriate method alternatives for hydrogen production. Based on these analysis results, it is strongly recommended that research and development activities should be improved to increase the efficiency and effectiveness of the renewable energy projects. With the help of this issue, it can be much easier to increase the performance of hydrogen production process. • Hydrogen production methods are evaluated for global energy transition. • A four-stage model has been proposed. • Selected criteria are weighted by using M-SWARA. • Method alternatives for hydrogen production are ranked via VIKOR. • Renewable energy expansion is the most important criteria. [ABSTRACT FROM AUTHOR]

It is widely accepted that there is an urgent need to make green hydrogen (GH2) projects financially viable to reduce global warming. However, any form of improvements to these GH2 projects lead to substantial cost increase. Due to this cost increase, making many improvements negatively affects the financial profitability of hydrogen projects. This is why there is a need for new advanced financial priority analysis tools so that it is easier to develop GH2 projects globally. Accordingly, the aim of this study is to identify and then define the most important factors affecting GH2 generation projects. To achieve this aim, this work proposes a new fuzzy multi-criteria decision-making model based on artificial intelligence (AI). First, experts are weighted with AI technique. Second, the missing evaluations are filled via a recommender system. Third, criteria weights are calculated by the M-SWARA technique integrated with quantum picture fuzzy rough (QPFR) sets. Finally, GH2 energy generation processes are listed by the QPFR-VIKOR approach. Overall, the main contribution of this study is the generation of a comprehensive AI oriented fuzzy decision-making model to make a detailed evaluation with respect to the financial potential improvements of the GH2 generation projects. The main originality of this model is the consideration of AI to calculate the weights of the criteria. Similarly, another benefit of the proposed model, that increases its superiority against other models, is the completion of missing evaluations by experts thanks to the recommender system. It is concluded that the most important criterion affecting green hydrogen investments is organizational effectiveness. • The most important factors of green hydrogen projects are defined. • The importance weights of experts are calculated with the AI technique. • M-SWARA is used to compute the criteria weights. • QPFR-VIKOR is considered to rank alternatives. • The most important criterion is organizational effectiveness. [ABSTRACT FROM AUTHOR]