Embedding Building Operation Cycles into Transformer Models for Indoor Temperature Prediction

Reliable forecasting of indoor thermal conditions is essential for optimizing HVAC control, reducing energy consumption, and ensuring occupant comfort in buildings. However, accurate long-term prediction of indoor temperature remains a major challenge due to temporal dependencies and variable building dynamics. In this study, we evaluated a range of models on the Smart Buildings Control Suite benchmark, spanning classical statistical approaches (spline regression, XGBoost) to advanced sequence architectures (encoder–decoder LSTM, PatchTST, Time-LLM). We further developed a domain-adapted variant of PatchTST that integrates Rotary Positional Embeddings (RoPE) aligned with building operation cycles, such as daily and weekly schedules. Results on a six-month validation window showed that classical baselines capture short-term dynamics but fail to maintain consistent accuracy over multi-week windows, while attention-based transformers substantially outperform recurrent and boosted-tree models. Most importantly, our RoPE-augmented PatchTST achieved the lowest mean absolute error (MAE=1.76 °F) in two-week forecasts across the six-month validation period, highlighting the importance of embedding building operation-specific temporal schedules into sequence models. These results indicate that domain-aware transformers can support reliable long-horizon indoor temperature prediction and thermal comfort management, ultimately facilitating more sustainable and energy-efficient building operation.