Inferential based statistical indicators for the assessment of solar resource data




Solar resource assessment, statistical comparison techniques, multivariate profile analysis, interval estimate plots


The drive to reduce fossil fuel dependency led to a surge in interest in renewable energy as a replacement fuel source, which provided research opportunities for vastly different domains. Statistical modelling was used extensively to assist in research. This study applied two statistical techniques that can be used in conjunction or independently to existing methods to validate solar resource data simulated from models. The case study, using a database from a Southern African Universities Radiometric Network,  provided illustrative benefits to the methods proposed, while comparing them with some of the validation methods currently used. It was demonstrated that profile analysis plots are easy to interpret, as deviations between modelled and measured data over time are clearly observed, while traditional validation scatter plots are unable to distinguish these deviations.



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Author Biographies

Gary Sharp, Nelson Mandela University

Assoicate Professor at the Nelson Mandela University Department of Statistics

Johan Hugo, Nelson Mandela University

Senior Lecturer at the Department of Statistics at the Nelson Mandela University

E. van Dyk, Nelson Mandela University

Professor at the Department of Physics at the Nelson Mandela University


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Statistical techniques to assess solar resource data, to better visualise and understand the distribution




How to Cite

Clohessy, C. M., Sharp, G., Hugo, J., & van Dyk, E. (2019). Inferential based statistical indicators for the assessment of solar resource data. Journal of Energy in Southern Africa, 30(1), 21–33.