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Found 5 papers, 1 papers with code
ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models
In response to the urgent demand for grid stability and the complex challenges posed by renewable energy integration and electricity market dynamics, the power sector increasingly seeks innovative technological solutions.
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning.
Applying Large Language Models to Power Systems: Potential Security Threats
Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency.
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection
However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training.
Super-Resolution Perception for Industrial Sensor Data
In this paper, we present the problem formulation and methodology framework of Super-Resolution Perception (SRP) on industrial sensor data.
