RE-LLM: Integrating Large Language Models into Renewable Energy Systems

2025-12-01

AI Summary

Energy system models produce highly technical outputs that hinder comprehension by non-expert stakeholders, limiting the practical deployment of scenario modeling in energy transitions. To address this, we propose a renewable energy system analysis framework integrating large language models (LLMs), featuring a novel three-tier hybrid architecture: optimization computation, machine learning agents, and natural language generation. This design accelerates model inference, enables multilingual interaction, and delivers real-time interpretation of policy implications. The framework is compatible with mainstream energy modeling platforms and substantially reduces computational overhead. It translates complex trade-offs, sensitivity analyses, and policy ramifications into accessible, context-aware natural language explanations. Empirical evaluation demonstrates significant improvements in model interpretability, stakeholder engagement, and decision-support effectiveness. By bridging the gap between data-driven analytics and actionable insights, the framework advances the operationalization of sustainable energy transition strategies.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Energy system models are increasingly employed to guide long-term planning in multi-sectoral environments where decisions span electricity, heat, transport, land use, and industry. While these models provide rigorous quantitative insights, their outputs are often highly technical, making them difficult to interpret for non-expert stakeholders such as policymakers, planners, and the public. This communication gap limits the accessibility and practical impact of scenario-based modeling, particularly as energy transitions grow more complex with rising shares of renewables, sectoral integration, and deep uncertainties. To address this challenge, we propose the Renewable Energy Large Language Model (RE-LLM), a hybrid framework that integrates Large Language Models (LLMs) directly into the energy system modeling workflow. RE-LLM combines three core elements: (i) optimization-based scenario exploration, (ii) machine learning surrogates that accelerate computationally intensive simulations, and (iii) LLM-powered natural language generation that translates complex results into clear, stakeholder-oriented explanations. This integrated design not only reduces computational burden but also enhances inter-pretability, enabling real-time reasoning about trade-offs, sensitivities, and policy implications. The framework is adaptable across different optimization platforms and energy system models, ensuring broad applicability beyond the case study presented. By merging speed, rigor, and interpretability, RE-LLM advances a new paradigm of human-centric energy modeling. It enables interactive, multilingual, and accessible engagement with future energy pathways, ultimately bridging the final gap between data-driven analysis and actionable decision-making for sustainable transitions.

Problem

• Bridging communication gap between technical energy models and non-expert stakeholders
• Enhancing interpretability of complex renewable energy transition scenarios
• Reducing computational burden while maintaining modeling rigor and accessibility

Innovation

• Integrates LLMs into energy modeling for interpretability
• Uses machine learning surrogates to accelerate simulations
• Generates natural language explanations for stakeholder communication