Non-intrusive appliance load monitoring using low-resolution smart meter data

• Published in: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) pp. 535 - 540
• Main Authors: Jing Liao, Elafoudi, Georgia, Stankovic, Lina, Stankovic, Vladimir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2014
• Subjects: Accuracy; Feature extraction; Hidden Markov models; Home appliances; Image edge detection; Libraries; Training
• DOI: 10.1109/SmartGridComm.2014.7007702

We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reproducible algorithmic description and benchmark the proposed methods with a state-of-the-art Hidden Markov Model (HMM)-based approach. Experimental results using three US and three UK households, show that both 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.