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RePower: An LLM-driven autonomous platform for power system data-guided research
Yu-Xiao Liu1, Mengshuo Jia2, Yong-Xin Zhang1
1State Key Laboratory of Power System Operation and Contral, Department of Electrical Engineering, Tsinghua University, Beijing 100084, P.R. China.
Patterns (New York, N.Y.)
|April 23, 2025
Summary
This study introduces RePower, an autonomous large language model (LLM) platform for power systems research. RePower independently conducts research, achieving significant error reductions in key tasks like power optimization.
Area of Science:
- Artificial Intelligence
- Power Systems Engineering
Background:
- Large language models (LLMs) show promise across scientific fields but have limited application in power systems research.
- Current power system studies often rely on human supervision for specific tasks.
Purpose of the Study:
- To introduce RePower, an autonomous LLM-driven research platform for power systems.
- To enable independent research, data acquisition, method design, and algorithm evolution in power systems.
Main Methods:
- Developed RePower, an autonomous LLM platform utilizing a reflection-evolution strategy.
- Applied RePower to critical data-driven power system tasks: parameter prediction, power optimization, and state estimation.
Main Results:
- RePower outperformed traditional methods in power system research tasks.
- Achieved an average error reduction of 29.07% across multiple tasks.
- Demonstrated a 39.78% error reduction in power optimization (0.00137 to 0.000825).
Conclusions:
- RePower facilitates autonomous scientific discovery in power systems.
- The framework promotes innovation by addressing complex, evaluable problems independently.
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