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dc.contributor.authorGao, Xiang
dc.date.accessioned2014-07-29 20:26:38 (GMT)
dc.date.available2014-07-29 20:26:38 (GMT)
dc.date.issued2014-07-29
dc.date.submitted2014-07-28
dc.identifier.urihttp://hdl.handle.net/10012/8588
dc.description.abstractPhotovoltaic (PV) applications worldwide are being increasingly deployed; however, the performance of PV systems is greatly affected by external factors, such as weather, environment, and terrain. These factors can form anomalies on solar panels, and lead to low performance. We present a data driven approach to identify anomalies on panels, based on the power output data and using our simplified solar irradiance model. The approach includes the in-plane solar irradiance model, disaggregation and detection of anomalies, and a decision tree to classify the types of the anomalies. The detection sensitivity is adjustable to suit different production environments. This methodology can be applied in multiple areas, such as anomaly alerts, energy loss analysis on different interval bases, and so on. The approach has been tested using real data collected in two cities in Ontario, Canada. The classification has a 85% precision rate for the detected anomalies.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectPhotovoltaic systemsen
dc.subjectAnomaly disaggregation and detectionen
dc.subjectClassificationen
dc.titleShould you clean your solar panels now?en
dc.typeMaster Thesisen
dc.pendingfalse
dc.subject.programManagement Sciencesen
uws-etd.degree.departmentManagement Sciencesen
uws-etd.degreeMaster of Applied Scienceen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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