Artificial intelligence-driven energy optimization in smart homes using interval-valued Fermatean fuzzy Aczel-Alsina aggregation operators

This research explores integrating artificial intelligence (AI) in energy optimization for smart homes and buildings, specifically focusing on using Aczel-Alsina aggregation operators within an interval-valued Fermatean fuzzy (IVFF) decision-making framework. The primary goal of this study is to develop a robust method for managing uncertainty and imprecision in energy optimization tasks. Using IVFF Aczel-Alsina operators, the proposed approach effectively aggregates decision information, making it highly adaptable to dynamic and uncertain environments. Through a series of comparative analyses, the study demonstrates that this method outperforms traditional techniques for handling complex, ambiguous data, resulting in more efficient energy consumption management and improved occupant comfort. The findings also highlight the advantages of AI-driven decision-making systems in smart buildings, offering a path to enhanced sustainability and environmental responsibility. The key contribution of this research lies in the novel application of IVFF Aczel-Alsina operators for energy optimization, which presents a more flexible and reliable solution compared to existing methods. This approach is poised to advance smart home technologies, ensuring optimal energy use while addressing uncertainties in realtime applications. Future research could focus on expanding the scope of real-time integration and exploring additional parameters for further refinement of the methodology.