Emam Walid, Waqas Hafiz Muhammad, Mahmood Tahir, Rehman Ubaid Ur, Pamucar Dragan
Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan.
Sci Rep. 2025 Apr 16;15(1):13083. doi: 10.1038/s41598-025-94340-3.
Artificial Intelligence (AI) based energy management systems utilize sophisticated AI algorithms to improve and control the consumption of energy in various sectors, such as power utilities, industrial systems, and smart buildings. These systems support real-time analysis of data, predictive analytics, and automatic adjustments to improve energy efficiency, reduce expenses, and lower environmental footprints. This research introduces a new method of AI-based energy management through the creation of advanced mathematical aggregation operators under the theory of hesitant bipolar complex fuzzy sets (HBCFSs). The generalized HBCFS theory is a complete decision-making model that can efficiently deal with uncertainties, hesitancy, and bipolar information in a complex setting. To solve the intrinsic difficulties of energy management decision-making, we propose a series of new HBCF Hamacher power aggregation operators. These operators improve the precision and stability of multi-attribute decision-making (MADM) processes by using the Hamacher t-norm and power aggregation rules to represent intricate interactions among decision attributes. Further, a comparative study is conducted to highlight the strength and superiority of the proposed aggregation operators that significantly contribute to AI energy management systems. The results establish that the method developed significantly improves the accuracy and reliability of decisions, warranting application in energy distribution and usage optimization.
基于人工智能（AI）的能源管理系统利用复杂的人工智能算法来改善和控制电力公用事业、工业系统和智能建筑等各个领域的能源消耗。这些系统支持数据的实时分析、预测分析和自动调整，以提高能源效率、降低成本并减少环境足迹。本研究通过在犹豫双极复模糊集（HBCFSs）理论下创建先进的数学聚合算子，引入了一种基于人工智能的能源管理新方法。广义HBCFS理论是一个完整的决策模型，能够在复杂环境中有效处理不确定性、犹豫性和双极信息。为了解决能源管理决策中的固有难题，我们提出了一系列新的HBCF哈马赫幂聚合算子。这些算子通过使用哈马赫t - 范数和幂聚合规则来表示决策属性之间的复杂相互作用，提高了多属性决策（MADM）过程的精度和稳定性。此外，进行了一项比较研究，以突出所提出的聚合算子的优势和优越性，这些算子对人工智能能源管理系统有显著贡献。结果表明，所开发的方法显著提高了决策的准确性和可靠性，值得应用于能源分配和使用优化。
