Non-Intrusive Signature Extraction for Major Residential Loads

A technique is needed to extract load signatures non-intrusively by using the smart meter data. Load signature extraction is different from load activity identification. For a target appliance whose signatures are to be extracted, the proposed technique should first select the candidate events that are likely to be associated with the appliance by using generic signatures and an event filtration step. He then applies a clustering algorithm to identify the authentic events of this appliance. In the third step, the operation cycles of appliances are estimated using an association algorithm. Finally, the electric signatures are extracted from these operation cycles. The results can have various applications. One is to create signature databases for the NILM applications. Another is for load condition monitoring. Validation results based on the data collected from three actual houses and a laboratory experiment have shown that the proposed method is a promising solution to the problem of load signature collection. The csv data file for this project can be download here: [login to view URL]~nipunb/iawe/[login to view URL] which is the zip file containing all the smart sensor data files in csv format on which machine learning has to be applied. The research paper along with the data is attached in the corresponding file