# LLM-DFL: Application of Large Language Model-Enabled Decision-Focused Learning in Local Energy Communities

> The research team from the University of Hong Kong proposed the LLM-DFL framework, which leverages the reasoning capabilities of large language models to optimize energy prediction models and significantly reduce the operational costs of local energy communities.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-07T16:44:41.000Z
- 最近活动: 2026-06-07T16:57:29.317Z
- 热度: 141.8
- 关键词: Large Language Model, Decision-focused Learning, Energy Optimization, Power Systems, Economic Dispatch, Forecasting, MILP, University of Hong Kong
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-dfl
- Canonical: https://www.zingnex.cn/forum/thread/llm-dfl
- Markdown 来源: floors_fallback

---

## Introduction: LLM-DFL Framework—Large Language Model-Enabled Energy Decision Optimization

The research team from the University of Hong Kong proposed the LLM-DFL framework, which innovatively combines the reasoning capabilities of large language models (LLMs) with Decision-Focused Learning (DFL) to address the goal misalignment issue in the traditional two-stage prediction-optimization process, significantly reducing the operational costs of local energy communities. This framework demonstrates obvious advantages in complex scenarios such as optimization with integer constraints and out-of-distribution scenarios.

## Problem Background: The Dilemma of Goal Misalignment Between Prediction and Decision-Making

Traditional energy optimization uses a two-stage prediction-optimization approach: first predict demand, then perform scheduling optimization. However, it has three major flaws: inconsistent goals (prediction optimizes for error, but actual focus is on cost), information loss (lack of decision context), and lack of feedback (the prediction model does not know its impact on decisions). DFL attempts to solve this problem, but traditional DFL faces difficulties in gradient calculation when dealing with integer constraints and non-convex problems.

## Core of the LLM-DFL Framework: Enhancing Decision-Focused Learning with LLMs

The core innovation of LLM-DFL is to enhance DFL using the reasoning capabilities of LLMs. Key insights: LLMs can understand domain knowledge, identify similar historical patterns, perform few-shot learning, and handle out-of-distribution scenarios. Framework flow: Input features → Basic prediction model → LLM correction (based on historical cases) → Optimization solution → Decision execution. Steps include constructing prompts, LLM outputting correction suggestions, and feeding back to the optimization model.

## Experimental Validation: Effectiveness and Advantages of LLM-DFL

The study designed four scenarios: NN+LP, Tree+LP, NN+MILP, and NN+SO. Results: LLM-DFL reduces the additional operational cost by an average of 2.38%-3.69%; it shows significant advantages in complex scenarios (Tree+LP, NN+MILP); it can adapt to out-of-distribution scenarios (such as sudden load changes during Christmas) without explicit labels. Computational cost: LLM inference takes 86.8-356.75 seconds, and each API call costs $0.010-$0.043, both of which are controllable.

## Research Significance: New Application of LLMs in Decision Optimization

Significance of LLM-DFL: 1. Breaks the prediction-optimization separation paradigm and emphasizes end-to-end optimization; 2. Expands LLMs to the field of numerical optimization and solves traditional gradient problems; 3. Demonstrates the value of prompt engineering; 4. Balances robustness and accuracy. It provides innovative ideas for the combination of LLMs and operations research optimization.

## Limitations and Future Directions

Limitations: High LLM inference latency, cost considerations for large-scale deployment, output uncertainty affecting reproducibility, and generalization to be expanded. Future directions: Lightweight LLMs/distillation to reduce latency, quantitative control of uncertainty, expansion to supply chain/financial scenarios, and combination with reinforcement learning for end-to-end training.