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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Knowledge enhanced framework for managing electricity generation and consumption in micro smart grids using Heronian
Zeeshan Ali1, Chiranjibe Jana2,3,4, Hamza Zafar1
1Department of Information Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan.
Scientific Reports
|May 30, 2026
Summary
This study introduces new models for managing electricity generation and consumption data in micro-smart grids. These methods enhance data integration and analysis for improved efficiency in industrial settings.
Area of Science:
- Electrical Engineering
- Computer Science
- Decision Science
Background:
- Modern industrial enterprises utilize extensive supplier networks for electricity generation and consumption.
- Efficient management of diverse data in micro-smart grids is crucial for production planning and scheduling.
Purpose of the Study:
- To develop novel models for managing complex data in micro-smart grids.
- To enhance decision-making processes for electricity generation and consumption.
Main Methods:
- Development of circular intuitionistic uncertain linguistic models.
- Design of four Heronian mean-based techniques (arithmetic, weighted arithmetic, geometric, weighted geometric).
- Construction of a multi-attribute border approximation area comparison technique.
Main Results:
- Proposed models effectively handle electricity generation and consumption data in micro-smart grids.
- Case studies demonstrate applications in node classification, scenario classification, and link prediction.
- The derived approaches show superiority and efficiency compared to prevailing models.
Conclusions:
- The developed circular intuitionistic uncertain linguistic models and associated techniques offer a robust framework for micro-smart grid management.
- These methods provide valuable tools for optimizing electricity generation and consumption, supporting industrial efficiency and data-driven decision-making.
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