AutoLFM: A multi-agent LLM framework for automated building load forecasting

Abstract Large language models (LLMs) exhibit significant potential in automating data-driven building load forecasting (BLF) model development, substantially reducing reliance on human effort and domain expertise. However, direct application of LLMs faces challenges, including the large and indivisible nature of optimization problems, slow optimization, error-prone code generation, and underutilization of LLM reasoning capabilities. This study introduces AutoLFM, a novel multi-agent framework leveraging LLMs to automate the end-to-end BLF model development workflow. AutoLFM decomposes the complex modeling process using a two-stage optimization strategy, with specialized LLM agents (Retriever, Reasoner, Coder, and Validator) performing distinct tasks. Key mechanisms include dynamic knowledge retrieval for prompt enhancement, data-adaptive search space generation by the Reasoner, and verification-enhanced code generation between the Coder and Validator. Experimental evaluations on three real-world building datasets demonstrate that AutoLFM efficiently generates BLF models, achieving predictive accuracy comparable to or exceeding manually designed baselines and other LLM-based methods, with an average R 2 improvement of 12.3%. It shortens the traditional development cycle from weeks to hours while achieving a 100% code generation success rate. Ablation study confirms the contributions of two-stage optimization, data-adaptive search space, and validation-enhanced code generation to the framework’s performance and reliability. AutoLFM highlights the potential of multi-agent LLM systems in automating complex time-series forecasting tasks, significantly reducing development time and dependence on specialized knowledge.