Why Sense needs time to detect device
Sense detects fluctuations in electrical energy use and our software utilizes machine learning to distinguish one appliance from another. The nature of machine learning requires a lot of data before accurate models can be formed which is why it takes time for device detection to improve. Sense needs to see your devices in its typical operating context in order to accurately identify them. Our data science team is constantly working to confirm and push out our device models to ensure they will take into account the variation between home to home, and even the same device running at different times.
I can see the device’s graph when I turn it on and off, why can’t I tag it?
Device detection is very much like trying to single out one person’s voice while 30 people are talking at the same time. It wouldn’t help to know that person’s voice in isolation from the crowd. In order to detect them, you would need listen hard through the crowd. Likewise, our homes are busy, electrically loud places. Sense needs to learn the signatures of your devices in the context of this cacophony. An isolated signature of a television turning on would offer little to help Sense identify your TV in the unique contexts in which it is often used. Here’s a video of one of our data scientists demonstrating how electrical signals are identified. Feel free to check out our relevant blog article: How Sense Learns About Your Devices?
How often do new devices get pushed?
Device detection updates are pushed automatically, and will vary from home to home based on what it has been learning. Sense needs to see many repetitions of a device working in your home before we can model it, and we’d only want to show you what we’re sure of.
So what can you do?
While you may not be able to train Sense, your input can still help by renaming devices, letting Sense know when devices get mistakenly marked as "On," and utilizing the Community Names feature when Sense can’t figure out a new device.
Relevant blog articles