RE-LLM: Integrating Large Language Models into Renewable Energy Systems
Ali Forootani, Mohammad Sadr, Danial Esmaeili Aliabadi, Daniela Thraen
TL;DR
RE-LLM presents a hybrid framework that embeds optimization-based scenario exploration within an LLM-enabled, human-centric interface. By coupling GAMS-based scenario analysis with machine-learning surrogates and SHAP interpretability, then translating results through grounded natural-language narration, the approach achieves orders-of-magnitude faster exploration while preserving fidelity. A Germany LULUCF case study demonstrates scalable surrogate performance, transparent feature attribution, and stakeholder-facing narratives that improve comprehension and decision-making. The work advances interactive, multilingual, and policy-relevant engagement in energy-system planning, and points to future extensions across multiple countries and larger scenario banks.
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.