Meta-Reinforcement Learning-Based Transferable Scheduling Strategy for Energy Management

Xiong, Luolin; Tang, Yang; Liu, Chensheng; Mao, Shuai; Meng, Ke; Dong, Zhaoyang; Qian, Feng

doi:10.1109/tcsi.2023.3240702

Cited by 10 publications

(1 citation statement)

References 47 publications

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“…Lv and Kumar [29] analyzed the dual-channel architecture defined by the wireless sensor software in 6G/IoE and proposed a reasonable solution to reduce the signal interference to transmit the related signals better. Xiong et al [30] proposed a transferable scheduling strategy for home energy management systems with different tasks utilizing a Meta-Reinforcement Learning framework. Zhao et al [31] proposed a learning-based method for surviving critical loads in microgrids during sequential extreme events.…”

Section: Electrical Power Edge-end Interaction Modelingmentioning

confidence: 99%

Meta In-Context Learning: Harnessing Large Language Models for Electrical Data Classification

Zhou,

Li,

Zhang

et al. 2023

Energies

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The evolution of communication technology has driven the demand for intelligent power grids and data analysis in power systems. However, obtaining and annotating electrical data from intelligent terminals is time-consuming and challenging. We propose Meta In-Context Learning (M-ICL), a new approach that harnesses large language models to classify time series electrical data, which largely alleviates the need for annotated data when adapting to new tasks. The proposed M-ICL consists of two stages: meta-training and meta-testing. In meta-training, the model is trained on various tasks that have an adequate amount of training data. The meta-training stage aims to learn the mapping between electrical data and the embedding space of large language models. In the meta-testing stage, the trained model makes predictions on new tasks. By utilizing the in-context learning ability of large language models, M-ICL adapts models to new tasks effectively with only a few annotated instances (e.g., 1–5 training instances per class). Our contributions lie in the new application of large language models to electrical data classification and the introduction of M-ICL to improve the classification performance with the strong in-context learning ability of large language models. Furthermore, we conduct extensive experiments on 13 real-world datasets, and the experimental results show that the proposed M-ICL improves the average accuracy over all datasets by 19.06%, 12.06%, and 6.63% when only one, two, and five training instances for each class are available, respectively. In summary, M-ICL offers a promising solution to the challenges of electrical data classification.

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Section: Electrical Power Edge-end Interaction Modelingmentioning

confidence: 99%