Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model
نویسندگان
چکیده
منابع مشابه
An unsupervised training method for non-intrusive appliance load monitoring
Non-intrusive appliance load monitoring is the process of disaggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an unsupervised training method for non-intrusive monitoring which, unlike existing supervised approaches, does not require training data to be collected by sub-metering individual appliances, nor does it require applianc...
متن کاملNon-Intrusive Load Monitoring
Non-Intrusive Load Monitoring (NILM) is a technique that determines the load composition of a household through a single point of measurement at the main power feed. Here we presented an unsupervised approach to determine the number of appliances in the household, their power consumption and the state of each one at any given moment.
متن کاملEvolving Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generat...
متن کاملAn Active Learning Framework for Non-Intrusive Load Monitoring: Preprint
Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimates the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too many data lacking class labels, but being unable to label the data manually for cost or time constraints. This paper presents an active learning fram...
متن کاملNon-Intrusive Load Monitoring Using Prior Models of General Appliance Types
Non-intrusive appliance load monitoring is the process of disaggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energies
سال: 2020
ISSN: 1996-1073
DOI: 10.3390/en13215629