Statistical Analysis and Data Mining

Synthesizing simulation and field data of solar irradiance

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Predicting the intensity and amount of sunlight as a function of location and time is an essential component in identifying promising locations for economical solar farming. Although weather models and irradiance data are relatively abundant, these have yet, to our knowledge, been hybridized on a continental scale. Rather, much of the emphasis in the literature has been on short-term localized forecasting. This is probably because the amount of data involved in a more global analysis is prohibitive with the canonical toolkit, via the Gaussian process (GP). Here we show how GP surrogate and discrepancy models can be combined to tractably and accurately predict solar irradiance on time-aggregated and daily scales with measurements at thousands of sites across the continental United States. Our results establish short-term accuracy of bias-corrected weather-based simulation of irradiance, when realizations are available in real space-time (eg, in future days), and provide accurate surrogates for smoothing in the more common situation where reliable weather data is not available (eg, in future years).