Solar Energy System Selection: A Pythagorean Fuzzy Rough Approach

Abstract

Existing approaches for multi-attribute decision-making rely solely on decision-makers' preferences and do not consider the degree of attribute prioritization. This paper introduces new prioritized aggregation operators within the Pythagorean fuzzy rough spectrum. These include the parameterized Schweitzer-Sklar t-norm and t-conorm to enhance variability in uncertain data and imprecision in applications. The Schweitzer-Sklar operators with adjustable parameters provide greater flexibility than traditional aggregation methods. Thus, the research presents a new class of prioritized aggregation operators in the Pythagorean fuzzy rough spectrum. The fundamental theoretical properties of these operators are thoroughly examined to verify their validity and potential uses. A novel multi-attribute decision-making approach using Pythagorean fuzzy rough sets to rank alternatives is developed. The proposed scientific approach is applied to a real-world solar energy system selection problem to demonstrate its practical utility and effectiveness. A comparison between the ranking results confirms the reliability and relevance of the proposed Pythagorean fuzzy rough approach.