Large Language Model-Empowered Interactive Load Forecasting

• URL: http://arxiv.org/abs/2505.16577v1
• Date: Thu, 22 May 2025 12:11:10 GMT
• Title: Large Language Model-Empowered Interactive Load Forecasting
• Authors: Yu Zuo, Dalin Qin, Yi Wang,
• Abstract summary: Large language models (LLMs) offer a new opportunity to bridge the gap between human operators and forecasting models.<n>A set of specialized agents is designed to perform different tasks in the forecasting workflow and collaborate via a dedicated communication mechanism.<n>Our experiments demonstrate that the interactive load forecasting accuracy can be significantly improved when users provide proper insight in key stages.
• Score: 3.0031348283981987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing complexity of power systems has made accurate load forecasting more important than ever. An increasing number of advanced load forecasting methods have been developed. However, the static design of current methods offers no mechanism for human-model interaction. As the primary users of forecasting models, system operators often find it difficult to understand and apply these advanced models, which typically requires expertise in artificial intelligence (AI). This also prevents them from incorporating their experience and real-world contextual understanding into the forecasting process. Recent breakthroughs in large language models (LLMs) offer a new opportunity to address this issue. By leveraging their natural language understanding and reasoning capabilities, we propose an LLM-based multi-agent collaboration framework to bridge the gap between human operators and forecasting models. A set of specialized agents is designed to perform different tasks in the forecasting workflow and collaborate via a dedicated communication mechanism. This framework embeds interactive mechanisms throughout the load forecasting pipeline, reducing the technical threshold for non-expert users and enabling the integration of human experience. Our experiments demonstrate that the interactive load forecasting accuracy can be significantly improved when users provide proper insight in key stages. Our cost analysis shows that the framework remains affordable, making it practical for real-world deployment.

Related papers

• Agentic Predictor: Performance Prediction for Agentic Workflows via Multi-View Encoding [56.565200973244146]
  Agentic Predictor is a lightweight predictor for efficient agentic workflow evaluation.<n>By learning to approximate task success rates, Agentic Predictor enables fast and accurate selection of optimal agentic workflow configurations.
  arXiv  Detail & Related papers  (2025-05-26T09:46:50Z)
• Example-Based Concept Analysis Framework for Deep Weather Forecast Models [25.56878415414591]
  We develop an example-based concept analysis framework, which identifies cases that follow a similar inference process as the target instance in a target model.<n>Our framework provides the users with visually and conceptually analogous examples, including the probability of concept assignment to resolve ambiguities in weather mechanisms.
  arXiv  Detail & Related papers  (2025-04-01T14:22:41Z)
• DCIts -- Deep Convolutional Interpreter for time series [0.0]
  The model is designed so one can robustly determine the optimal window size that captures all necessary interactions within the smallest possible time frame.<n>It effectively identifies the optimal model order, balancing complexity when incorporating higher-order terms.<n>These advancements hold significant implications for modeling and understanding dynamic systems, making the model a valuable tool for applied and computational physicists.
  arXiv  Detail & Related papers  (2025-01-08T08:21:58Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks [50.75902473813379]
  This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models. The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes.
  arXiv  Detail & Related papers  (2024-07-04T14:36:49Z)
• LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
  We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer. We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
  arXiv  Detail & Related papers  (2024-04-03T23:57:34Z)
• Concept Learning for Interpretable Multi-Agent Reinforcement Learning [5.179808182296037]
  We introduce a method for incorporating interpretable concepts from a domain expert into models trained through multi-agent reinforcement learning. This allows an expert to both reason about the resulting concept policy models in terms of these high-level concepts at run-time, as well as intervene and correct mispredictions to improve performance. We show that this yields improved interpretability and training stability, with benefits to policy performance and sample efficiency in a simulated and real-world cooperative-competitive multi-agent game.
  arXiv  Detail & Related papers  (2023-02-23T18:53:09Z)
• Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction [31.02081143697431]
  Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and video-surveillance applications. We propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. We employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations.
  arXiv  Detail & Related papers  (2022-04-25T11:12:37Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patents [0.0]
  We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance. We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility. We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
  arXiv  Detail & Related papers  (2020-03-30T13:06:25Z)

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.