The internal temperature of a building is important – particularly in offices and work environments –for maximizing comfort and productivity. Managing the temperature is also essential for reducing the energy consumption of a building. In the US, buildings account for around 29% of total end-use energy consumption, with more than 40% of this energy dedicated to managing the internal temperature of a building via heating and cooling.
The human body is sensitive to both radiative and convective heat. The convective part revolves around humidity and air temperature, whereas radiative heat depends upon the surrounding surface temperatures inside the building. Understanding both thermal aspects is key for balancing energy consumption with occupant comfort. However, there are not many practical methods available for measuring the impact of radiative heat inside buildings. Researchers from the University of Minnesota Twin Cities have developed an optical sensor that could help solve this problem.
Limitation of thermostats for radiative heat
Room thermostats are used in almost every building today to regulate the internal temperature and improve the comfort levels for the occupants. However, modern thermostats only measure the local air temperature and don’t account for the effects of radiant heat exchange between surfaces and occupants, resulting in suboptimal comfort levels and inefficient energy use.
Finding a way to measure the mean radiant temperature in real time inside buildings could provide a more efficient way of heating the building – leading to more advanced and efficient thermostat controls. Currently, radiant temperature can be measured using either radiometers or black globe sensors. But radiometers are too expensive for commercial use and black globe sensors are slow, bulky and error strewn for many internal environments.
In search of a new approach, first author Fatih Evren (now at Pacific Northwest National Laboratory) and colleagues used low-resolution, low-cost infrared sensors to measure the longwave mean radiant temperature inside buildings. These sensors eliminate the pan/tilt mechanism (where sensors rotate periodically to measure the temperature at different points and an algorithm determines the surface temperature distribution) required by many other sensors used to measure radiative heat. The new optical sensor also requires 4.5 times less computation power than pan/tilt approaches with the same resolution.
Integrating optical sensors to improve room comfort
The researchers tested infrared thermal array sensors with 32 x 32 pixels in four real-world environments (three living spaces and an office) with different room sizes and layouts. They examined three sensor configurations: one sensor on each of the room’s four walls; two sensors; and a single-sensor setup. The sensors measured the mean radiant temperature for 290 h at internal temperatures of between 18 and 26.8 °C.
The optical sensors capture raw 2D thermal data containing temperature information for adjacent walls, floor and ceiling. To determine surface temperature distributions from these raw data, the researchers used projective homographic transformations – a transformation between two different geometric planes. The surfaces of the room were segmented into a homography matrix by marking the corners of the room. Applying the transformations to this matrix provides the surface distribution temperature on each of the surfaces. The surface temperatures can then be used to calculate the mean radiant temperature.
The team compared the temperatures measured by their sensors against ground truth measurements obtained via the net-radiometer method. The optical sensor was found to be repeatable and reliable for different room sizes, layouts and temperature sensing scenarios, with most approaches agreeing within ±0.5 °C of the ground truth measurement, and a maximum error (arising from a single-sensor configuration) of only ±0.96 °C. The optical sensors were also more accurate than the black globe sensor method, which tends to have higher errors due to under/overestimating solar effects.
The researchers conclude that the sensors are repeatable, scalable and predictable, and that they could be integrated into room thermostats to improve human comfort and energy efficiency – especially for controlling the radiant heating and cooling systems now commonly used in high-performance buildings. They also note that a future direction could be to integrate machine learning and other advanced algorithms to improve the calibration of the sensors.
This research was published in Nature Communications.
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