MaPL-LLM: Building energy forecasting with lightweight language models

Accurate building energy forecasting is essential for efficient energy management and sustainable building operation. While recent studies suggest that large language models (LLMs) exhibit strong potential for time-series forecasting, their application to building energy prediction remains limited by high computational cost, inefficient parameter utilization, and inadequate modeling of multivariate dependencies. To overcome these challenges, this paper proposes MaPL-LLM, a lightweight LLM-based forecasting framework that integrates multivariate prompt fusion and temporal patching. MaPL-LLM adopts a frozen LLaMA-1B backbone and introduces two complementary modules: (1) a multivariate prompt-based embedding module that encodes temporal context and statistical characteristics into structured textual prompts, and (2) a multivariate patching-based numerical embedding module that captures local temporal patterns and cross-variable interactions. Only lightweight input transformation and output projection layers are trained, significantly improving computational efficiency while maintaining stable performance. Extensive experiments on multiple building types from the BDG2 dataset demonstrate that MaPL-LLM consistently outperforms state-of-the-art methods, including TimeXer, PatchTST, and TFDFNet. For a 24-step forecasting horizon, MaPL-LLM achieves an MAE of 0.166, an MSE of 0.072, and an R² of 0.946, reducing forecasting error by over 10% compared with the strongest baseline. Moreover, training and inference time are reduced by more than 60% relative to existing LLM-based methods, highlighting its suitability for scalable and real-time building energy management applications.