Saeed Muhammad Haris, Saeed Muhammad, Rahman Atiqe Ur, Ahsan Muhammad, Mohammed Mazin Abed, Marhoon Haydar Abdulameer, Nedoma Jan, Martinek Radek
Department of Mathematics, University of Management and Technology, Lahore, 54000, Pakistan.
Department of Chemistry, University of Management and Technology, Lahore, 54000, Pakistan.
Heliyon. 2024 Jun 27;10(13):e33464. doi: 10.1016/j.heliyon.2024.e33464. eCollection 2024 Jul 15.
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
在过去十年中，随着对环境保护以及可持续、优化资源管理的关注度不断提高，对可再生能源的需求显著增加。由于传统能源生产来源正以惊人的速度枯竭，并造成持久的环境破坏，探索绿色且具有成本效益的方法来满足能源需求至关重要。由于每个国家都有不同的地理、政治、社会和自然因素，因此出现了应利用哪种可再生能源进行优化资源管理的问题。通过应用基于动态模糊超软集的方法来评估当前部署的可再生能源系统，并为土耳其基于上述因素安装新的可再生能源系统提供决策支持系统，解决了这一多准则决策（MCDM）挑战。由于新可再生能源项目的安装和旧项目的评估受人为判断的显著影响，这主要由于专家的心理因素而留下了很大的不确定性空间。首先结合手头问题讨论了模糊超软集（FHSs）的新概念及其熵（EN）和基于TOPSIS的运算。所提出的结构优于文献中的结构，因为它允许将数据参数作为子参数值访问，同时利用模糊结构的通用性来处理不确定性。该技术在任何环境中都有很大潜力作为潜在的决策支持系统。目前，使用假设的专家评级来说明动态结构的工作原理，并进行敏感性分析以研究排序中的主要标准权重。使用我们的方法对当前部署的可再生能源系统进行评估后发现，与传统方法相比，系统性能平均有所提高。此外，基于地理、政治、社会和自然因素的新项目安装决策支持系统显示出整体系统效率有潜在提高。这些数值结果突出了我们的方法在优化资源管理和促进可持续能源实践方面的有效性和实际适用性。
