Occupancy sensors and occupancy data analytics have promised a lot over the last decade but the number of commercial buildings that actually use them remains low. A 2019 study by Yamanda et al. estimated that only 6% to 10% of commercial buildings are equipped with occupancy sensors, showing just a 5% improvement from the start of the decade. Furthermore, the majority of the increase was from the low occupancy warehouse and storage sector, which saw an increase from 1% to 34% from 2010 to 2015, while education and office buildings sectors actually saw a decrease, from 9% to 8% and from 14% to 8% respectively. Few would doubt the potential of the technology but something is preventing its widespread adoption.

More often than not, occupancy technology is adopted as an energy-efficiency strategy in buildings eager to achieve the cost-saving and environmental benefits promised. However, while many tests show the energy-saving potential of the technology, few offer methods that can be easily reproduced on a wide scale in the real world. As a result, it becomes nearly impossible for buildings and enterprises to predict the performance of such sensors in their specific application and, in practice, deployments commonly fail to meet energy-savings expectations. A recent study by the Pacific Northwest National Laboratory (PNNL) focuses on exactly that.

“The lack of a fully described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy-sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones,” reads the August paper: A Review Of Existing Test Methods For Occupancy Sensors.

Motivated by the desire to fairly characterize the range of new promising occupancy technologies entering the market, the report presents the results of a literature review of test methods for characterizing occupancy-sensor performance, as well as research articles containing ad-hoc test methods. The review consolidates test conditions for characterizing sensor performance in indoor spaces and identifies apparent test method gaps that need to be filled in order to evaluate emerging technologies and products. The process is intended to enable the development of a future technology-agnostic test method that facilitates occupancy-sensor performance characterization in a way that better represents the performance of real-world buildings.

“Recurring investigations have observed that energy savings have fallen short of manufacturer claims, especially in general office spaces,” reads the PNNL paper. “This failure to meet energy-saving projections has been attributed to varying causes, including the occupancy sensor not being installed or maintained in accordance with manufacturer recommendations, manufacturer claims of typical performance that are either exaggerations (by as much as 83%) or not appropriate for a specific application, or occupants choosing to disable or remove underperforming sensors instead of reconfiguring or replacing them.”

The research highlights the reality of the technology in real-world scenarios, where occupants resist the privacy and work-hindering effects of the technology by bypassing or removing sensor technology completely. Furthermore, poor sensor placement, misconfiguration, and fundamental limitations of the sensor technology itself all play their part in the poor energy-saving performance of occupancy technology in real-world deployments. The disappointment of not meeting the exaggerated expectations that were based on manufacturers’ claims only serves to frustrate adopters, which then has negative impacts on the widespread impression of the technology. Even the newest products fail to provide accurate realistic figures, further deepening the problem.

“Innovative occupancy sensors, some of them combining multiple sensing technologies, have come on the market over the years, with claims of improved performance compared to their predecessors. However, in practice, their performance has neither differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them, nor has it led to high deployment or high user satisfaction in human-occupied spaces with persistent presence,” the PNNL paper continues.

“While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them typically have been loosely described and have incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology.”

Despite these issues holding back the adoption of occupancy technology, the demand for energy-saving and better space utilization has never been greater. While COVID-19 has put much of the world’s commercial real estate on hold, the crisis will eventually subside and we will return to the trends that have been developing for decades — workplace densification and the demand for occupant-centric workplaces. Exaggerated claims may be holding back the industry but steadily improving technology and the growing demand for workplace optimization will continue to drive growth in the market in the years ahead.

“With office densification rates increasing across the world, combined with evidence of poor space utilization and the expectations of occupants for more human and productive environments, the need for workspace management platforms to provide better insight into the repurposing of current workplaces has never been so urgent,” states our 2020 occupancy report. “We estimate that the Occupancy Analytics market in Commercial Office space achieved systems sales of $2.17 Billion in 2019, rising to $5.73 Billion by 2024, growing at a CAGR of 21.5%.”