The smart thermostat represents a common entry point in home automation. I know several people who were not into home automation until they purchased homes with smart thermostats already installed. The thermostats were the start of a home automation journey for many of them.
The crazy thing about smart thermostats is their ability to artificially learn. In fairness, some entry level models don’t come equipped with artificial learning but are still considered ‘smart’ because they can be accessed remotely and programmed to automatically adjust the temperature. But if you have one that artificially learns, you have a pretty amazing device.
How does a smart thermostat learn? Vivint explains that a device’s learning capabilities are tied to two important things: sensors and machine learning algorithms. Incidentally, Vivint customers can include a smart thermostat in a home automation system that is professionally installed and monitored.
Sensors and Data Collection
Smart thermostats come equipped with sophisticated programming that takes advantage of deep learning algorithms. But in order for those algorithms to do what they should do, they need information. The information comes from sensors.
An array of sensors collects data that gets fed into the algorithms. Thermostats themselves have embedded sensors measuring ambient temperature and limited movement. When connected to a comprehensive home automation system, the thermostats can also gather data from indoor motion sensors, exterior weather sensors, lights, and so on.
All the data is fed to the algorithms to help the thermostat’s electronic brain learn a homeowner’s routine. Combining a learned routine with external data from online sources is what makes it possible for smart thermostats to self-adjust.
Learning Through Pattern Recognition
A typical smart thermostat needs to be programmed when it is first set up. Programming gives the device a baseline to start with. The thermostat will take sensor data and compare it to the established program with the goal of recognizing patterns indicating a homeowner’s behavior. Deep learning capabilities help the thermostat figure out when homeowners are away, the temperatures they prefer when home, and more.
Adjusting On-the-Fly
The goal of gathering data and identifying patterns is to equip the thermostat to adjust on-the-fly. Over time, data collection and pattern analysis enable the embedded software to refine its adjustment strategies so that the thermostat can self-adjust for maximum efficiency and homeowner comfort.
It can be more difficult for a smart thermostat to adjust to a homeowner who doesn’t keep a regular schedule. But given enough time, most thermostats can do an adequate job of accommodating whatever schedule a homeowner does keep.
Reinforcement Learning
Another tool smart thermostats utilize is something known as reinforcement learning. Simply put, the thermostat will make an adjustment and then measure the results. If the results are positive, the behavior is reinforced and repeated.
Here is an example: a thermostat reduces the temperature just before the homeowner goes to bed. If the system uses less energy and the homeowner doesn’t manually override the adjustment, the change is considered positive. But if the homeowner consistently puts the temperature back up, the change is negative. Either result will influence the thermostat’s future adjustments.
Smart thermostats are amazing devices when they come equipped with deep learning algorithms and the right sensors for data collection. However, do not make the mistake of thinking they are capable of human thought. In the end, they are still just man-made devices that can only behave as they are programmed to.As amazing as smart thermostats are, they are limited in their capabilities. Such will always be the case. So feel free to expect great things from your smart thermostat. Just don’t expect miracles.
