Abstract
A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo eve (KL) expan- sion 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 ap- pliance 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 Pos- teriori (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: tracebase and reference energy disaggregation data set (REDD). The pre- sented 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
Highlights
Load monitoring techniques determine the turned on appliances and their individual energy consumption within a given period of time [1]
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
The turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Posteriori (MAP) estimation
Summary
Load monitoring techniques determine the turned on appliances and their individual energy consumption within a given period of time [1]. Recent work reported in [5, 10, 13] on NILM methods are based on very few smart meter measurement parameters with a low sampling rate They focus on constructing signatures of residential appliances based solely on time domain information. In [13] a state of the art Hidden Markov Model (HMM) is used for disaggregation of turned on appliances based on low sampled active power measurements This is an unsupervised method that uses expert knowledge to set initial models for states of known appliances, the models’ reliable operation depends on correctly setting the priori-values for each state of each appliance and using a training set where appliance operation does not overlap.
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