Non-intrusive load monitoring based on low frequency active power measurements

• Main Authors: Chinthaka Dinesh, Roshan Indika Godaliyadda, Mervyn Parakrama B. Ekanayake, Janaka Ekanayake, Pramuditha Perera
• Format: Article
• Language: English
• Published: AIMS Press 2016-03-01
• Series: AIMS Energy
• Subjects: Non-intrusive load monitoring (NILM); appliance identification; energy disaggregation; smart grid; smart meter; uncorrelated spectral information; subspace component; window clustering; appliance usage pattern; priori probability
• Online Access: http://www.aimspress.com/energy/article/708/fulltext.html

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on active power signal is presented. This method works effectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the <em>Karhunen Loéve</em> (KL) expansion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible appliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Posteriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: <em>tracebase</em> and reference energy disaggregation data set (REDD). The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real households.