Nonparametric Conditional Heteroscedastic Hourly Probabilistic Forecasting of Solar Radiation

John Boland and Adrian Grantham
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John Boland: Centre for Industrial and Applied Mathematics, School of ITMS, University of South Australia, Mawson Lakes 5095, Australia
Adrian Grantham: Centre for Industrial and Applied Mathematics, School of ITMS, University of South Australia, Mawson Lakes 5095, Australia

J, 2018, vol. 1, issue 1, 1-18

Abstract: We develop a new probabilistic forecasting method for global horizontal irradiation (GHI) by extending our previous bootstrap method to a case of an exponentially decaying heteroscedastic model for tracking dynamics in solar radiance. Our previous method catered for the global systematic variation in variance of solar radiation, whereas our new method also caters for the local variation in variance. We test the performance of our new probabilistic forecasting method against our old probabilistic forecasting method at three locations: Adelaide, Darwin, and Mildura. These locations are chosen to represent three distinct climates. The prediction interval coverage probability, prediction interval normalized averaged width and Winkler score results from our new probabilistic forecasting method are encouraging. Our new method performs better than our previous method at Adelaide and Mildura; regions with a higher proportion of clear-sky days, whereas our previous method performs better than our new method at Darwin; a region with a lower proportion of clear-sky days. These results suggest that the ideal probabilistic forecasting method might be climate specific.

Keywords: solar radiation; forecasting; probabilistic forecasting; nonparametric; exponential smoothing; conditional heteroscedastic (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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