ImputeLLM: A prompt-free large language model framework for robust time-series imputation in HVAC systems

Missing data poses a critical challenge in the modeling and control of HVAC systems, where reliable time-series information is essential for energy optimization and fault detection. While deep learning has advanced imputation accuracy, existing models often struggle with robustness under high missing rates or require extensive fine-tuning. This study introduces ImputeLLM, a novel imputation framework that integrates a frozen large language model (LLM) encoder with a Transformer-based decoder and an adaptive hybrid loss function. Without any fine-tuning or prompt engineering, the model efficiently encodes masked time-series data into semantic embeddings and reconstructs missing values with high accuracy. The framework is validated using real-world monitoring data from a central cooling plant in Qingdao, China, under MCAR, MAR and MNAR masking conditions. Compared to conventional methods, the proposed approach achieves up to 37.7% improvement in MAPE over linear interpolation and shows 16.5% gain over traditional MAE-based losses. Furthermore, it demonstrates strong generalization across varying missing rates and feature observability levels. These results highlight the potential of LLM-based architectures for practical deployment in energy systems with noisy or incomplete data.