Sense needs to see devices in their usual context with their regular usage pattern to be able to accurately identify them. That means that “training” Sense by turning devices on/off and labeling them is not an effective method to help Sense learn. You can learn more about the challenges of implementing a “learning” mode here.
While you cannot “train” Sense in such a way, we have introduced a variety of features that take advantage of user input to help improve device detection in your home and for the entire userbase.
- Network Identification allows Sense to see some of the simple “handshake” messages put out by your networked devices.
- Integrations with smart bulbs from Philips Hue and smart plugs from TP-Link Kasa and Belkin Wemo will net you instant detections for connected devices and provides great data to the Data Science team.
- Renaming your devices, supplying the make/model, taking advantage of the Community Names feature, and filling out your Home Details feeds the Data Science team great data that improves detection for everybody.
- When Sense finds a device, but you’re finding the detections to be inaccurate, you can report it as “not on.” This feeds our Data Science team valuable information so they can continue to refine the detection model.
Remember, even without native detections, you can still take advantage of Sense insights. The Power Meter is a fantastic tool that provides a real-time view of your energy consumption. Try turning on and off your devices while watching in the Power Meter, to identify how much they consume. You can do the same for the “Always On” devices in your home, identifying how much they’re costing you every day.