:warning: This repository is no longer actively maintained. It previously dealt with Non-Intrusive Load Monitoring (NILM), focusing on predicting household appliance status from aggregated power load data. We explored different thresholding methods and evaluated deep learning models for regression and classification tasks.
Implements clustering methods to choose the right thresholds for each appliance. The clustering allows for more than two states.