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FPE-LLM: Highly Intelligent Time-Series Forecasting and Language Interaction LLM in Energy Systems

Autor: Qiu, Zihang, Li, Chaojie, Wang, Zhongyang, Mo, Huadong, Xie, Renyou, Chen, Guo, Dong, Zhaoyang

This paper introduces Fusion PEFT Energy LLM (FPE-LLM), a large language model (LLM) fine-tuned for energy system forecasting using a combination of Prefix and Lora Parameter-Efficient Fine-Tuning (PEFT) methods. FPE-LLM addresses three key challenge

Externí odkaz: http://arxiv.org/abs/2411.00852

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Large Language Model-aided Edge Learning in Distribution System State Estimation

Autor: Xie, Renyou, Yin, Xin, Li, Chaojie, Chen, Guo, Liu, Nian, Zhao, Bo, Dong, Zhaoyang

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to o

Externí odkaz: http://arxiv.org/abs/2405.06999

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Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks

Autor: Li, Yi, Xie, Renyou, Li, Chaojie, Wang, Yi, Dong, Zhaoyang

Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion. However, exist

Externí odkaz: http://arxiv.org/abs/2405.00742

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