Qaisar, I.; Sun, K.; Zhao, Q.; Xing, T.; Yan, H. Multi-Sensor-Based Occupancy Prediction in a Multi-Zone Office Building with Transformer. Buildings2023, 13, 2002.
Qaisar, I.; Sun, K.; Zhao, Q.; Xing, T.; Yan, H. Multi-Sensor-Based Occupancy Prediction in a Multi-Zone Office Building with Transformer. Buildings 2023, 13, 2002.
Qaisar, I.; Sun, K.; Zhao, Q.; Xing, T.; Yan, H. Multi-Sensor-Based Occupancy Prediction in a Multi-Zone Office Building with Transformer. Buildings2023, 13, 2002.
Qaisar, I.; Sun, K.; Zhao, Q.; Xing, T.; Yan, H. Multi-Sensor-Based Occupancy Prediction in a Multi-Zone Office Building with Transformer. Buildings 2023, 13, 2002.
Abstract
Buildings are responsible for approximately 40% of the world’s energy consumption and 36% of the total carbon dioxide emissions. Building occupancy is essential, enabling Occupant-Centric Control for zero emissions and decarbonization. Although existing machine learning and deep learning methods for building occupancy prediction have achieved remarked progress, their analyses remain limited when applied to complex real-world scenarios. Besides, there is a high expectation for Transformer algorithms to predict building occupancy accurately. Therefore, this paper presents an Occupancy Prediction Transformer network (OPTnet). We fuse and feed multi-sensor data (building occupancy, indoor environmental conditions, HVAC operations) into a Transformer model to forecast the future occupancy presence in multiple zones. We perform experimental analysis and compare it to different occupancy prediction methods (e.g., Decision Tree, Long Short-Term Memory networks, Multi-layer perceptron) and diverse time horizons (1,2,3,5,10,20,30mins). The performance metrics (e.g., Accuracy and Mean Squared Error) are employed to evaluate the effectiveness of prediction algorithms. Our OPTnet method achieves superior performance on our experimental two-week data compared to existing methods. The improved performance signifies its potential to enhance HVAC control systems and energy optimization strategies. We will make the code publicly available at https://github.com/kailaisun/occupancy-prediction-binary to promote transparency and reproducibility.
Keywords
Occupancy prediction; Deep learning; Multi-sensor fusion; Transformer
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
