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

• URL: http://arxiv.org/abs/2411.00852v1
• Date: Wed, 30 Oct 2024 11:22:37 GMT
• Title: FPE-LLM: Highly Intelligent Time-Series Forecasting and Language Interaction LLM in Energy Systems
• Authors: Zihang Qiu, Chaojie Li, Zhongyang Wang, Huadong Mo, Renyou Xie, Guo Chen, Zhaoyang Dong,
• Abstract summary: Fusion PEFT Energy LLM (FPE-LLM) is a large language model (LLM) fine-tuned for energy system forecasting. FPE-LLM addresses three key challenges in the energy system and LLM fields.
• Score: 5.218730690088186
• License:
• Abstract: 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 challenges in the energy system and LLM fields: 1. Enhancing few-shot learning for handling extreme environmental conditions. FPE-LLM can leverage both textual and time-series data to achieve accurate predictions in few-shot contexts. 2. Reducing dependence on expert input to improve efficiency. FPE-LLM can provide guidance and results on related problems, acting like an expert system. Even non-experts can use FPE-LLM to complete all tasks related to forecasting and its associated tasks. 3. Mitigating hallucination risks through standardized fine-tuning. We validated this through multi-task learning and the self-reasoning characteristics of LLMs. Our research opens the door to fully realizing the intelligent potential of FPE-LLM in the energy forecasting field. With the injection of more knowledge and data, FPE-LLM is expected to replace a significant amount of manual work and contribute to the stability and efficiency of energy forecasting.

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