In the quest to make buildings smarter and more energy-efficient, researchers are turning their attention to the often-overlooked factor of occupant behavior. A comprehensive review published in *Next Energy* (which translates to *Next Energy* in English) sheds light on the latest advancements in occupancy detection and monitoring technologies, offering a roadmap for the future of energy management in buildings.
Buildings consume a significant chunk of global energy, and occupant behavior plays a pivotal role in this consumption. Traditional methods of monitoring occupancy, such as CO₂ sensors and passive infrared (PIR) sensors, have laid the groundwork, but emerging technologies are taking this a step further. “Advanced methods, particularly those leveraging deep learning and computer vision, are facilitating more accurate, occupant-driven energy management,” explains Wenjie Song, lead author of the study and a researcher at the Department of Architecture and Built Environment, University of Nottingham.
The study delves into a variety of innovative approaches, from probabilistic models like Hidden Markov Models (HMMs) to deep learning architectures such as Convolutional Neural Networks (CNNs). These technologies have shown impressive accuracy in both controlled environments and real-world settings. Emerging transformer-based fusion architectures and vision-language models (VLMs) are also making waves, promising to capture complex spatial-temporal occupancy patterns and enabling multimodal, interpretable occupancy detection.
However, the path to widespread adoption is not without its challenges. Privacy concerns, data security, and user acceptance are significant hurdles that need to be addressed. “Personalization and adaptability are key, especially in multi-occupant contexts,” Song notes. Multi-sensor data fusion is another area of focus, aiming to enhance detection stability and reduce false positives.
From a commercial perspective, the economic feasibility of these technologies is crucial. Installation costs and the energy savings achieved through occupancy-driven heating, ventilation, and air-conditioning (HVAC) optimization are key considerations for large-scale implementation. The potential for significant energy savings makes this research particularly relevant for the energy sector, offering a pathway to more efficient and sustainable building management.
As the field evolves, the integration of these advanced technologies could revolutionize how we interact with our built environment. By balancing energy efficiency, comfort, and privacy, researchers and practitioners can develop smart, occupant-centric building systems that pave the way for a more sustainable future. This review not only synthesizes current methods and identifies research gaps but also proposes future directions, guiding the development of next-generation smart buildings.