Advancing energy management using q-rung orthopair fuzzy prioritised multiparametric information measures

The concept of q-rung orthopair fuzzy set (q-ROFS) is more useful in processing ambiguous information in multicriteria decision-making scenarios than the Pythagorean fuzzy set and intuitionistic fuzzy set. The main feature of q-ROFS data is that the membership grade, non-membership grade, and the sum of their qth-powers all lie in the unit closed interval $[0,1]$. This feature provides decision-makers with a multitude of possibilities when deciding which grades to assign to the objects in the universe. This study presents new prioritised multiparametric information measures as well as their mathematical modelling and implementation for three-parameter q-ROFSs. Together with the corresponding similarity measures (SMs), the information measures multiparametric distance measure, multiparametric weighted distance measure, multiparametric prioritised distance measure, and multiparametric prioritised distance measure based on priority degree are proposed. In addition, some important instances are provided to discuss the practicality. We use the proposed SMs as the foundation for a unique multicriteria decision-making method. To further illustrate the efficacy and value of new SMs in energy management problems and energy-efficient technologies for a building project, two numerical examples are provided. We compare optimal outcomes with those of a few different SMs and aggregation operators to confirm the usefulness of the proposed approach.