Abstract
Artificial intelligence (AI)-enabled energy efficiency in buildings leverages AI to optimize energy consumption, enhancing sustainability and aiding decision-makers in selecting cost-effective and environmentally friendly solutions. Pythagorean fuzzy Z̃ number sets, capable of handling high uncertainty, play a crucial role in representing fuzzy information in decision-making. This research introduces novel operational laws and weighted aggregation operators, focusing on neutral addition and scalar multiplication. Comprehensive investigations into the properties of these laws lead to groundbreaking aggregation operators tailored for Pythagorean fuzzy Z̃ number information. These include weighted, ordered weighted, and hybrid neutral averaging aggregation operators, along with a novel approach for calculating attribute weights. Relationships and characteristics of these operators are explored, and an algorithm for solving multiple attribute group decision-making problems is provided. A practical example illustrates the approach’s superiority, supported by comparative results.
Published Version
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