Technical assessment of solar energy storage investments with recommender system-enhanced quantum picture fuzzy rough sets

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems Pub Date : 2024-11-10 DOI:10.1016/j.ijepes.2024.110361

Gang Kou , Hasan Dinçer , Serhat Yüksel , Serkan Eti , Merve Acar

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引用次数: 0

Abstract

The performance of solar energy storage projects should be improved by taking appropriate actions. However, there are very different criteria that affect the performance of these investments. Therefore, businesses need to focus on more important criteria to use the budget effectively and efficiently. This situation increases the need for a priority analysis for performance indicators of solar energy storage investments. Accordingly, the purpose of this study is to make evaluation for the technical assessment of solar energy storage investments. In this scope, a new four-stage model is introduced by considering different decision-making techniques and fuzzy sets. The first stage is related to the prioritizing the experts with artificial intelligence (AI)-based decision-making method. Secondly, the missing evaluations of solar energy storage investments are estimated with expert recommender system. In the following part, the criteria for the technical assessment of solar energy storage investments are weighted by quantum picture fuzzy rough sets (QPFRS) adopted M−SWARA. The final stage consists of ranking the solar energy storage alternatives with QPFR-VIKOR. The main contribution of this study is the generation of the decision matrix by the help of AI. This situation gives an opportunity to calculate the significance weights of the experts. Therefore, the analysis results can be more reliable and coherent. It is concluded that battery capacity is the most critical factor for the technical assessment of solar energy storage investments. On the other hand, pumped hydro for mechanic energy is found as the most significant solar energy storage alternative. Governments should provide the necessary incentives for the development of high-capacity battery technologies. In this context, tax reductions can be provided to companies that invest in production technologies. This contributes to the cost efficiency of businesses.

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利用推荐系统增强的量子图模糊粗糙集对太阳能储存投资进行技术评估

应通过采取适当行动提高太阳能储存项目的性能。然而，影响这些投资绩效的标准大不相同。因此，企业需要关注更重要的标准，以便有效和高效地使用预算。在这种情况下，更有必要对太阳能储能投资的绩效指标进行优先分析。因此，本研究的目的是对太阳能储能投资的技术评估进行评价。在此范围内，通过考虑不同的决策技术和模糊集，引入了一个新的四阶段模型。第一阶段是利用基于人工智能（AI）的决策方法对专家进行优先排序。其次，利用专家推荐系统估算太阳能储能投资的缺失评价。接下来，采用 M-SWARA 的量子图模糊粗糙集（QPFRS）对太阳能储能投资的技术评估标准进行加权。最后，利用 QPFR-VIKOR 对太阳能储能备选方案进行排序。本研究的主要贡献在于借助人工智能生成了决策矩阵。这种情况为计算专家的重要性权重提供了机会。因此，分析结果可以更加可靠和一致。结论是，电池容量是太阳能储能投资技术评估的最关键因素。另一方面，用于机械能的抽水蓄能被认为是最重要的太阳能储能替代方案。各国政府应为开发高容量电池技术提供必要的激励措施。在这方面，可以为投资生产技术的公司提供税收减免。这有助于提高企业的成本效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Electrical Power & Energy Systems 工程技术-工程：电子与电气

CiteScore

12.10

自引率

17.30%

发文量

1022

审稿时长

51 days

期刊介绍： The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces. As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.