Energy Management Using Real-Time Non-Intrusive Load Monitoring

The sketch is configured to update the metrics every eight seconds. You can see the Arduino sketch below and in the project's GitHub, NILM⁹ The actual energy disaggregation computations are hosted on a Raspberry Pi 4 which is connected over USB to the Arduino to fetch the aggregate metrics. The computations are comprised of running the tflite appliance inference models, trained and quantized per the steps described above, with pre- and post-processing steps. The inference models output predicted energy for each appliance from 599-sample sized windows of the aggregate apparent power input signal. These predictions are stored in a local CSV file and made available for downstream reporting and analysis.