Our reliance on electronic devices and appliances has never been higher, so when the power goes out, it can quickly become an unpleasant and inconvenient situation, especially for those who are unable to prepare in time. To help combat this problem, Roni Bandini has devised a device he calls “EdenOff,” which is placed inside an electrical outlet and utilizes machine learning at the edge to intelligently predict when an outage might occur.

Developed with the use of Edge Impulse, Bandini began by creating a realistic dataset that consisted of three columns that pertain to different aspects of an outlet: its voltage, the ambient temperature, and how long the service has been working correctly. After training a model based on one dataset for regular service and the other for a failure, his model achieved an excellent F-1 score of .96, indicating that the model can forecast when an outage might take place with a high degree of accuracy. 

Bandini then deployed this model to a DIY setup by first connecting a Nano 33 BLE Sense with its onboard temperature sensor to an external ZMPT101B voltage sensor. Users can view the device in operation with its seven-segment display and hear the buzzer if a failure is detected. Lastly, the entire package is portable thanks to its LiPo battery and micro-USB charging circuitry.

For more details on this project, you can watch its demonstration video below and view its public project within the Edge Impulse Studio.