Database Establishment for Machine Learning in NILM
نویسندگان
چکیده
Nonintrusive load monitoring (NILM) is a problem of identifying operating appliances and estimating their energy consumptions based on whole home electric signals. Machine learning concepts and methods have been gradually applied to tackle NILM. A key factor of enabling and advancing machine learning methods in any problem is the availability of proper databases. The Reference Energy Disaggregation Data Set (REDD) is one such initiative example for NILM. In this paper, we extend from this initiative to address three key properties: informative, diverse, and scalable for a database. These properties enable a broader range of application and research of machine learning methods in NILM. The importance of data sets consisting of single appliance, intermediate aggregate appliances, and whole home recordings in developing machine learning methods is also discussed.
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