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.

Related papers

• The Price of Prompting: Profiling Energy Use in Large Language Models Inference [5.254805405012678]
  This paper introduces MELODI, a framework crafted to monitor and analyze the energy consumed during large language models inference processes. The dataset, generated using MELODI, encompasses a broad spectrum of LLM deployment frameworks, multiple language models, and extensive prompt datasets. Our findings indicate substantial disparities in energy efficiency, suggesting ample scope for optimization and adoption of sustainable measures.
  arXiv  Detail & Related papers  (2024-07-04T12:16:28Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• F-LMM: Grounding Frozen Large Multimodal Models [53.8059045627934]
  We present F-LMM -- grounding frozen off-the-shelf LMMs in human-AI conversations. Using only a few trainable CNN layers, we can translate word-pixel attention weights to mask logits. Our F-LMM neither learns special segmentation tokens nor utilises high-quality grounded instruction-tuning data.
  arXiv  Detail & Related papers  (2024-06-09T15:14:26Z)
• Not All Attention is Needed: Parameter and Computation Efficient Transfer Learning for Multi-modal Large Language Models [73.48675708831328]
  We propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs) The Efficient Attention Skipping (EAS) method evaluates the attention redundancy and skips the less important MHAs to speed up inference. The experiments show that EAS not only retains high performance and parameter efficiency, but also greatly speeds up inference speed.
  arXiv  Detail & Related papers  (2024-03-22T14:20:34Z)
• Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper [14.081178100662163]
  This paper delves into the pressing need in. -Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs) Our position paper highlights current states and the necessity of further studying into the topic.
  arXiv  Detail & Related papers  (2023-11-22T03:28:34Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models [12.708117108874083]
  Large Language Models (LLMs) generate code snippets given natural language intents in zero-shot, i.e., without the need for specific fine-tuning. Previous research explored In-Context Learning (ICL) as a strategy to guide the LLM generative process with task-specific prompt examples. In this paper, we deliver a comprehensive study of. PEFT techniques for LLMs under the automated code generation scenario.
  arXiv  Detail & Related papers  (2023-08-21T04:31:06Z)
• Automated Few-Shot Time Series Forecasting based on Bi-level Programming [5.760976250387322]
  This paper develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bi-level programming perspective. Comprehensive experiments fully demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML framework.
  arXiv  Detail & Related papers  (2022-03-07T12:15:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.