An inference framework for detection of home appliance activation from voltage measurements

We present an inference framework for automatic detection of activations of home appliances based on voltage envelope waveforms. We cast the problem of appliance detection and recognition as an inference problem. When the activation signatures are known, the problem reduces to a simple detection problem. When the activation signatures are unknown, the problem is reformulated as a blind joint delay estimation. Due to the non-convexity of the negative log-likelihood, finding a global optimal solution is a key challenge. Here, we introduce a novel algorithm to estimate the activation templates, which is guaranteed to yield an error within a factor of two of that of the optimal solution. We apply our method to a real-world dataset consisting of voltage waveform measurements of several appliances obtained in multiple homes over a few weeks. Based on ground truth data, we present a quantitative analysis of the proposed algorithm and alternative approaches.