Power Disaggregation for Low-sampling Rate Data
نویسندگان
چکیده
In this paper, we focus on energy disaggregation at low-sampling rates (at 6sec and 1min) and use only active power measurements for training and testing. Specifically, we develop two algorithms: one is a low-complexity, supervised approach based on Decision Trees and another is an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. These are benchmarked with a state-of-the-art Hidden Markov Model (HMM)-based approach. Experimental results using the REDD dataset as well as data collected from a real UK household, show that the two proposed methods outperform the HMM-based approach and are capable of disaggregating a range of domestic loads even when the training period is very short.
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