Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Phathutshedzo Mpfumali, Caston Sigauke (), Alphonce Bere and Sophie Mulaudzi
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Phathutshedzo Mpfumali: Department of Statistics, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa
Alphonce Bere: Department of Statistics, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa
Sophie Mulaudzi: Department of Physics, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa

Energies, 2019, vol. 12, issue 18, 1-28

Abstract: Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.

Keywords: forecast combination; lasso via hierarchical interaction; partially linear additive models; probabilistic solar irradiance forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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