Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking

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...

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Main Author: Barbieri, Florian Benjamin Eric
Format: Thesis
Published: Curtin University 2019
Online Access:http://hdl.handle.net/20.500.11937/77126
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author Barbieri, Florian Benjamin Eric
author_facet Barbieri, Florian Benjamin Eric
author_sort Barbieri, Florian Benjamin Eric
building Curtin Institutional Repository
collection Online Access
description 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.
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format Thesis
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institution Curtin University Malaysia
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last_indexed 2025-11-14T11:09:30Z
publishDate 2019
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spelling curtin-20.500.11937-771262019-12-05T05:36:15Z Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking Barbieri, Florian Benjamin Eric 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. 2019 Thesis http://hdl.handle.net/20.500.11937/77126 Curtin University fulltext
spellingShingle Barbieri, Florian Benjamin Eric
Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
title Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
title_full Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
title_fullStr Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
title_full_unstemmed Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
title_short Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking
title_sort random finite sets based very short-term solar power forecasting through cloud tracking
url http://hdl.handle.net/20.500.11937/77126