Sense clips two current transformers onto the main panel leads and samples at 1 MHz, analysing the high-frequency current signatures that uniquely identify each appliance’s motor and switching characteristics. Device detection uses a supervised neural network trained on millions of hours of home energy data to match signatures to appliance archetypes without manual labelling by the homeowner. Real-time 1-second power updates in the Sense app enable users to watch energy spikes as appliances switch on, building intuition about electricity consumption. Integration with Philips Hue, Amazon Alexa, and smart plug protocols enables automation — for example, turning off a forgotten lamp detected by Sense when you leave home. Historical analysis generates monthly device-level cost breakdowns, identifying the most expensive appliances for behaviour-change coaching.
IoT Product
Energy
Sense Labs
Sense is a whole-home energy monitor that uses machine learning to disaggregate individual appliance consumption from the main panel current waveforms, identifying your refrigerator, dryer, HVAC, and EV charger automatically.