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A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on uncorrelated spectral components of an active power signal is presented. This method utilizes the Karhunen Loéve (KL) expansion to breakdown the active power signal into subspace components so as to construct a unique information rich appliance signature. Unlike existing NILM techniques that rely on multiple measurements at high sampling rates, this method works effectively with a single active power measurement taken at a low sampling rate. After constructing the signature data base, subspace component level power conditions were introduced to reduce the number of possible appliance combinations. Then, an algorithm was presented to identify the turned on appliance combination in a given time window. After identifying the turned on appliance combination, another algorithm was introduced to disaggregate the energy contribution of each individual appliance. The case study conducted using tracebase public data set demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations that contain single state, multi state and continuous varying appliances. Finally, the proposed method was modified to accommodate usage behavior patterns of each residence adaptively. The modification was validated using six US households in REDD public database. This significantly improves the convergence speed of the turned on appliances identification process.

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