Translution: A Hybrid Transformer–Convolutional Architecture with Adaptive Gating for Occupancy Detection in Smart Buildings

Occupancy detection is vital for improving energy efficiency, automation, and security in smart buildings. Reliable detection systems enable dynamic control of lighting, heating, ventilation, air conditioning, and security operations, leading to substantial cost savings and enhanced occupant comfort. However, accurately detecting occupancy using environmental sensor data remains challenging. Existing machine learning and deep learning models, such as Random Forests, convolutional neural networks, and recurrent neural networks, often struggle to capture both fine-grained local patterns and long-range temporal dependencies, limiting their generalization to complex, real-world occupancy patterns. To address these challenges, we propose Translution, a novel hybrid Transformer-based architecture specifically designed for occupancy detection from multivariate sensor time-series data. Translution combines multi-scale convolutional encoding to extract local temporal features, self-attention mechanisms to model long-range dependencies, and an adaptive gating mechanism that dynamically selects relevant features to improve robustness and generalization. We trained Translution on 8143 samples and evaluated it on two distinct subsets of the University of California, Irvine (UCI) Occupancy Detection Dataset: one with shorter, more consistent time spans (2804 samples) and another covering longer, more varied occupancy cycles with abrupt changes and different lighting/ventilation conditions (9752 samples). Evaluating these diverse subsets, which represent both typical and challenging real-world scenarios, explicitly strengthens Translution’s generalizability claim, demonstrating its ability to detect occupancy across varied temporal patterns and environmental conditions accurately. Our results demonstrate that Translution achieves 98.5% accuracy, 97.3% F1-score, and 98.55% area under the receiver operating characteristic curve, significantly outperforming traditional machine learning and deep learning baselines. These findings highlight Translution’s potential as a highly accurate and stable solution for real-time occupancy detection in diverse smart building environments.