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

• Main Authors: Qiu, Zihang, Li, Chaojie, Wang, Zhongyang, Mo, Huadong, Xie, Renyou, Chen, Guo, Dong, Zhaoyang
• Format: Journal Article
• Language: English
• Published: 30.10.2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Learning
• DOI: 10.48550/arxiv.2411.00852

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.