Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
dc.contributor.author | Barbieri, Florian Benjamin Eric | |
dc.contributor.supervisor | Arindam Ghosh | en_US |
dc.date.accessioned | 2019-12-05T05:36:15Z | |
dc.date.available | 2019-12-05T05:36:15Z | |
dc.date.issued | 2019 | en_US |
dc.identifier.uri | http://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.publisher | Curtin University | en_US |
dc.title | Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | PhD | en_US |
curtin.department | School of Electrical Engineering, Computing, and Mathematical Sciences | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Science and Engineering | en_US |