Development of Solar Irradiance Decomposition Models in Six Climatological Zones in South Africa

Abstract Separated irradiance components are used to calculate the plane-of-array irradiance which is necessary for accurately predicting the energy output of solar photovoltaic (PV) systems. Solar irradiance decomposition or separation models are required for calculating the diffuse fraction ( K d ), direct normal irradiance (DNI), and diffuse horizontal irradiance (DIF) from global horizontal irradiance (GHI) measurements. To improve K d estimates, we introduce a novel, simple logistic multipredictor model. The new model only requires measured GHI and calculable predictors: clear-sky GHI, a clearness index ( K t ), apparent solar time, solar zenith angle, deviation in clear-sky K t , a clear-sky index, a persistence index, and a cloud enhancement index. Using 1-yr quality-controlled 1-min data of K d , DIF, and DNI from 14 stations spread across all 6 climatological regions of South Africa, the new model and the other 14 previously developed K d models are comprehensively validated. Error metrics and a mean linear ranking method determine the best model for estimating K d , DNI, and DIF, based on the sky condition, site, climate zone, and overall, in South Africa. Under all skies, the new model is proven to provide the most accurate K d , DIF, and DNI estimates. The new model, Engerer3, and Louche models are superior at estimating DNI, K d , and DIF under clear skies, respectively. In overcast skies, the nested model is superior at estimating K d , while the Yang4 model is well suited for predicting DIF and DNI. To ascertain the new model’s broader applicability, we recommend validation in other climate zones and geographical locations. Significance Statement This study is conducted in a region where solar decomposition models have not been developed at a very high temporal resolution and with a diverse spectrum of weather systems due to altitude variations and wide proximity ranges to coastlines. The presence of six unique climatic zones in South Africa underscores regional diversity, yet the study’s results hold applicability beyond the southern African context. Decomposition modeling is an essential stage in the model chain and, hence, extremely valuable for solar resource assessment and forecasting. Given that final uncertainties in solar resource estimates correlate with financial risks in utility-scale projects, a reduction of uncertainty in decomposition models is critical for reducing the uncertainty of solar photovoltaic (PV) energy forecasts.