Unsupervised Clustering of Residential Electricity Consumption Measurements for Facilitated User-Centric Non-Intrusive Load Monitoring
Publication: Computing in Civil and Building Engineering (2014)
Abstract
Non-intrusive load monitoring (NILM) is a low-cost alternative to appliance level sub-metering, that leverages signal processing and machine learning techniques to estimate the power consumption of individual appliances from whole-home measurements. However, the difficulty associated with obtaining training data sets for the commonly used supervised NILM classification algorithms is a major obstacle in wide commercial adoption of the technology. The diversity of electrical load signatures (patterns of appliances' power draw) demands in-situ training (labeling of the signatures), which often needs to be performed by ordinary users through user-system interaction. To produce the example signatures required for training, continuous interaction with users might be required, which could reduce the success of the training process due to user fatigue. Pre-populating the training data set could help facilitate the process by reducing the number of user-system interactions needed for labeling. Taking into consideration all the issues described above, a study to test the feasibility of autonomous clustering of similar appliances' signatures based on hierarchical clustering was investigated. The information contained in the structure of the binary cluster tree was used for clustering without the need for a priori selection of the number of clusters. The assessment, carried out on data collected from a residential setting, showed promising results (with accuracy above 90%, calculated based on the ground truth labels) supporting the feasibility of the approach for unsupervised clustering.
Get full access to this article
View all available purchase options and get full access to this chapter.
Information & Authors
Information
Published In
Copyright
© 2014 American Society of Civil Engineers.
History
Published online: Jun 17, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.