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dc.contributor.authorBarbieri, Florian Benjamin Eric
dc.contributor.supervisorArindam Ghoshen_US
dc.date.accessioned2019-12-05T05:36:15Z
dc.date.available2019-12-05T05:36:15Z
dc.date.issued2019en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/77126
dc.description.abstract

Tracking clouds with a sky camera within a very short horizon below thirty seconds can be a solution to mitigate the effects of sunlight disruptions. A Probability Hypothesis Density (PHD) filter and a Cardinalised Probability Hypothesis Density (CPHD) filter were used on a set of pre-processed sky images. Both filters have been compared with the state-of-the-art methods for performance. It was found that both filters are suitable to perform very-short term irradiance forecasting.

en_US
dc.publisherCurtin Universityen_US
dc.titleRandom Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Trackingen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Electrical Engineering, Computing, and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US


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