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A novel algorithmic multi-attribute decision-making framework for the evaluation of energy systems using rough
Muhammad Abdullah1, Khuram Ali Khan1, Jaroslav Frnda2
1Department of Mathematics, University of Sargodha, Sargodha, 40100, Pakistan.
Heliyon
|December 11, 2024
Summary
This study introduces a new hypersoft rough set (HSRS) framework for selecting the optimal energy system (ESS). HSRS effectively handles complex data and uncertainties, leading to more robust decision-making for power deficits.
Area of Science:
- Decision Science
- Information Theory
- Energy Systems Analysis
Background:
- Selecting optimal energy systems (ESS) is complex, involving legal, economic, environmental, and viability factors.
- Traditional decision frameworks (fuzzy, soft sets) struggle with data complexity, integrity, and uncertainty in ESS selection.
- Existing methods are often inadequate for handling rough data and ensuring robust decision-making for power deficits.
Purpose of the Study:
- To introduce a novel theoretical framework, hypersoft rough set (HSRS), integrating rough set and hypersoft set concepts.
- To develop a rigorous algorithmic strategy for assessing ESS feasibility using HSRS operations.
- To address limitations in existing models for optimal energy system selection (ESS) under uncertainty and incomplete data.
Main Methods:
- Integration of rough set theory for ambiguity and uncertainty with hypersoft set theory for incomplete data analysis.
- Characterization of elementary notions, approximation spaces, lower/upper approximations, and operations within the HSRS framework.
- Development and application of a novel algorithmic strategy for ESS feasibility assessment based on HSRS.
Main Results:
- The proposed HSRS framework demonstrates enhanced versatility, discrimination ability, and suitability for handling data abnormalities.
- A practical application in Pakistan successfully identified the ideal ESS, validating the algorithm's adaptability and effectiveness.
- The HSRS approach proved more robust compared to existing methods for optimal energy system selection.
Conclusions:
- The hypersoft rough set (HSRS) framework offers a powerful and robust analytical tool for complex decision-making in energy system selection.
- HSRS provides a superior method for addressing data uncertainties and incompleteness, crucial for tackling electric power deficits.
- The study validates the practical utility and enhanced performance of HSRS in real-world energy system optimization challenges.
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