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November 10, 2022
Occupancy sensors are an underrated smart building feature that can make both new and old buildings more energy efficient, and more comfortable for occupants. This article covers everything you need to know about them, including 3 highly effective building applications and the different types of occupancy sensors.
With a growing population, more urbanization, and an increasing demand for energy efficient buildings as climate change dominates as a primary concern, the demand for smarter buildings is increasing. Commercial buildings consume more than 19% of total energy in the US. HVAC systems account for 27% of this energy use, and lighting accounts for 31%, so minimizing the amount of energy used by commercial buildings can make a big difference.
Both new builds and retrofit projects can save energy by implementing “smart” technologies. One of these technologies is occupancy sensors. Occupancy sensors are being used more and more often to save energy in lighting and HVAC systems, as well as improve the comfort of occupants. They’re simple to install, use, and maintain.
But how can you use occupancy sensors to save energy? What is their actual impact? And is it worth it to include them in your unique building or project? Think of this article as your ultimate guide to occupancy sensors; we’ll cover what they are, their benefits, disadvantages or security concerns, how much energy they save, and how they actually save energy.
Article Contents:
Choosing Hardwired or Wireless
Applications for Occupancy Sensors
Occupancy sensors (also called motion sensors, presence sensors, or vacancy sensors) are sensors that monitor a physical space to determine if there’s anyone present in that space.
Some of them also include people counting capabilities (making them people counting sensors as well as motion sensors). Occupancy sensors can be connected to building systems, and enable lighting or HVAC (for example) to be turned on or off depending on the presence of people in a space. Lighting can also be dimmed based on occupancy, if that would be preferred (depending on if the lights are dimmable or not). Something called bi-level (or dual stage) dimming is very popular in parking garages, for example, because it’s not ideal for lights to be completely turned off in a space like this, but dimming can still help parking garages save energy. Essentially, bi-level dimming is when lights are set to a low brightness by default but, when motion is detected, the brightness of the lights in a space is increased.
Occupancy sensors are not only used to detect if building systems should be turned on, off or modulated, they are also used for data collection. When occupancy sensors collect data, and if this data is utilized properly, it can be used to help people be more productive and efficient as well. For example, occupancy sensor data can be used to create cleaning schedules based on how often spaces are used. This way, rooms that are used every day are cleaned more frequently and thoroughly (like bathrooms), and spaces that are hardly used at all can have a less frequent cleaning schedule (like the office of a coworker on vacation).
One might assume that only new building projects can be smart buildings, but this is not the case. Occupancy sensors can be easily implemented in both new builds and retrofit projects to add smart features, like automation, to lighting and HVAC systems. This is good news, since retrofitting an existing building is more cost effective and sustainable than demolishing and replacing a building.
There are a few different types of occupancy sensors, and the type you choose depends on what you’re trying to achieve.
This is the most common type of occupancy sensor partly because of its simplicity and anonymous (and secure) method of detecting presence. It’s a “passive” (rather than “active”) sensor that monitors for space occupancy by detecting when a warm object (like a person or animal) moves into its field of view. These sensors can also be used to generate anonymous heat maps. PIR sensors do not work when they are obstructed by obstacles, like glass walls.
These “active” (rather than passive) sensors emit a high-frequency sound that humans and animals cannot hear, and they have a microphone to detect the echo. Based on the amount of time it takes for the reflected sound to reach the microphone, the sensor can tell if there is a person moving through its field of view. Ultrasonic sensors can cover larger areas than PIR sensors, but they are also more sensitive, so they can often be falsely triggered by something like wind, or changes in ventilation.
Microwave sensors have a couple advantages over PIR sensors: they detect motion consistently over all temperatures, and have a coverage of 360 degrees (whereas PIR sensors typically have a coverage of 90 degrees). However, since they can sense movement through walls, they might be too sensitive to accurately be used in a smaller, indoor space. Microwave sensors operate by emitting microwave signals (don’t worry, they’re completely safe), so they are considered an “active” sensor. They sense motion by measuring the time taken for the signal to be reflected back to the sensor (this is known as echo time). This is similar to the way the ultrasonic sensor works, but instead of using sound waves, the microwave sensor uses electromagnetic waves.
Acoustic sensors are another type of sensor, but they aren’t used very often because they are also prone to false triggering. They detect people in a space by monitoring sound (such as keyboard typing), but they can often be set off by background noises, like an ambulance driving by outside a building. Some sensors are “hybrid” sensors, meaning they use multiple types of detection technologies. Most of these types of sensors rely on a minimum of two types of detection to determine if a space is occupied, but they only require the confirmation of one type of detection to keep lights (for example) “on”.
Sensors can also either be wired or wireless. Wired sensors are considered faster and more reliable than wireless sensors because wired sensors are not impacted by distance or wireless interference. This helps to improve the speed and consistency of their data collection. However, they are also much more expensive and complicated to install. The cost of wired sensors can go up depending on the amount of wire needed to connect them to building systems, like lighting or HVAC. Additionally, the price of hiring a qualified professional to hardwire them into your building can also be steep.
On the other hand, wireless sensors have batteries that need to be periodically replaced, but advancements in wireless device technology have been made so that most sensors have a battery life of 10+ years. The main benefit of wireless sensors, like the Cence occupancy sensor, is that there is very little installation cost. Installation can be as easy as peel and stick on a wall or ceiling, then sensors are remotely programmed to be grouped with lighting or HVAC systems.
Occupancy sensing can save energy, but there are also many other benefits to implementing occupancy sensors in a commercial building. Our society is coming out of a pandemic, and people are starting to return to the office, so now is the time to get a better understanding of how our physical spaces are being used. Occupancy sensors can help us do this by collecting data on building, room and desk utilization. This data can be analyzed to form intelligent cleaning schedules, office layouts ideal for productivity and more.
In a 2011 study, the Lawrence Berkeley National Laboratory released a meta-analysis of energy savings from lighting controls in commercial buildings, and found that occupancy sensing saves about 24% in lighting applications. Occupancy sensors also help keep the energy consumption for lighting and HVAC systems in commercial buildings in line with energy codes; “most commercial building energy codes require lighting be turned OFF or reduced when it is not being used”. Occupancy sensing can save energy via a few different methods; including through demand controlled ventilation and lighting.
Demand controlled ventilation (DCV) is enabled by occupancy sensors. HVAC systems are sized for the maximum quantity of occupants in a space, but this full performance isn’t necessary when a space hasn’t reached its maximum capacity. This is where DCV comes in. By using occupancy sensors and people counting sensors, you can collect real-time data on space use, and automate HVAC systems to only work as hard as is necessary to provide proper ventilation and ideal temperatures. DCV is made better with indoor environmental quality (IEQ) sensors, which can collect data on temperatures and other parameters related to the indoor environment and air quality of a space. As an example, a large gathering in an enclosed space can get very warm. If IEQ sensors are present to sense the temperature, that data can be used to automatically boost ventilation and AC in a space to bring it back to an ideal temperature. How would this work with occupancy sensors? You could base HVAC controls on occupancy by automating systems to boost ventilation and AC when there are more people in a space, and vice versa. In fact, you can reduce energy costs by 10% to 40% a year by adjusting the building’s ventilation based upon actual occupancy. Demand controlled lighting works the same way; lighting can be automated to be turned on when a space is occupied, and off once a person is no longer sensed in a space.
Because of the energy that can be saved with occupancy sensors, their implementation provides 1 point towards a LEED certification, and you can receive even more points with DCV.
Occupancy sensors not only make lighting and HVAC systems more efficient, they can also help to make people more productive. Let’s dive into that next.
So far, we’ve covered how occupancy sensors are useful for detecting when and how much lighting and ventilation should be applied. The next level up from this is to use their data collection capabilities to benefit the occupants of a space.
Imagine this: your employees have started to return to the office after the pandemic, and you’ve prepared by distributing a handful of anonymous, secure occupancy sensors throughout your floor in hopes of understanding how people intuitively utilize the office. By doing so, you can move offices and conference rooms accordingly, and move desks and meetings to locations that are more comfortable for employees. You know that the more comfortable people are, the less distractions they’ll have, and their productivity could be improved. So, how does this work?
PIR occupancy sensors can collect anonymous, secure data, and send it to a Digital Twin (building manager can review visualized occupancy data, trends, and potentially even actionable insights generated by AI technology. This information can then be used to optimize the layout of a space for productivity, as well as provide real-time availability data for meeting rooms and desks. With availability data, employees can see which rooms, desks and more are occupied and vacant for their convenience. Using occupancy data like this, improves the experience of people working or living in a space, and can also help to identify which parts of a building are uncomfortable, and can be optimized with DCV and improved indoor environmental quality in general.
Most buildings have set cleaning schedules based on a consistent day or time. These cleaning schedules are straightforward, but often inefficient. Cleaning schedules are often created with the busiest day of the year for a building in mind (aka when a building is most in need of cleaning). With this schedule, when the cleaners come in for daily or weekly cleanings, they clean every room as if it’s been the busiest day of the year. This way, on the actual busiest day of the year, the building is clean. Goal, achieved! Right? Actually, when we think about the rest of the year, the building doesn’t truly require nearly as much cleaning as on that fateful, super busy day. This is where dynamic cleaning schedules come in.
Dynamic cleaning schedules are like demand controlled ventilation or lighting, but for cleaning. Basically (with occupancy sensors), one can base a building’s cleaning schedule on whether or not specific rooms or levels need to be cleaned. For example, bathrooms are high traffic rooms that are sometimes difficult to keep sanitary and pristine. If anonymous, secure occupancy sensors are used in bathrooms, data could be collected about the busiest time of day for them, and cleaning could be scheduled for directly after that time. Using a dynamic schedule like this is a more efficient way to keep bathrooms clean, and minimize occupant complaints. There are many other ways that you can use occupancy sensors to keep buildings clean. In fact, you’re really only limited by your creativity and analytics software when it comes to using occupancy data.
If you do decide that occupancy sensors could benefit a building you manage, the main thing to keep in mind is that raw data without actionable insights is useless to most people. Occupancy sensors are great for collecting data but, in addition to them, one also needs a way to monitor data, view visualized trends (in the form of heat maps, graphs etc.), and obtain actionable insights from that data. By connecting occupancy sensors to a cloud app (ideally integrated with a digital twin), data can be transmitted to the app via a wireless mesh network, then analyzed and interpreted with software on the app.
When we say that the cloud app would analyze, we mean to say specifically that the integrated software would look at correlations, causations, and trends among data points from the sensors, and actionable insights could be generated based off of these trends. For example, if a meeting room is determined to have had high traffic (perhaps, 10 or more people depending on how the software defines “high-traffic”), then the app could notify users that a room needs to be cleaned, or account for the busiest days and times when generating a dynamic cleaning schedule.
By implementing occupancy sensors to get a true picture of how a space is used, energy usage, office layouts and cleaning schedules can therefore be made more efficient. When cleaning schedules are made more efficient, for example, the time of maintenance and cleaning staff can be used more productively. Dynamic cleaning schedules will additionally result in more comfortable occupants, and demand controlled lighting and HVAC systems save energy.
Digital twin: a digital twin is a digital representation of something physical, like a digital floor plan. It can be automatically made more accurate when more data is collected about a physical space or object.
If your cleaning schedule could be optimized, your meeting rooms and desks are underutilized, or your energy bill is just too high, we hope we’ve inspired you to keep occupancy sensors in mind as a potential solution.
When they’re paired with a Digital Twin, data collected by occupancy sensors can be used to create trend graphs, and analyzed to provide actionable insights. If you’re interested in learning more about how to install occupancy sensors in a commercial or multi-family residential building, check out the Cence website. We provide a variety of wireless sensors, including occupancy, people counting, IEQ and indoor air quality (IAQ) sensors. We additionally have a cloud app so that you can actually make use of that collected data by reviewing the automatically generated actionable insights.
Feel free to contact us to learn more, or check out our YouTube channel to learn more about our technology.
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