Synthetic generation of plausible solar years for long-term forecasting of solar radiation

Abstract

In this work, we test and improve an algorithm proposed in previous studies to generate synthetic series of plausible solar years (PSY). The method provides 100 synthetic years of coupled global horizontal irradiance (GHI) and direct normal solar irradiance (DNI) in 1-min resolution. The algorithm uses 10–20 years of hourly coupled GHI + DNI datasets that can be retrieved for most of the locations of the world from satellite estimates. The method consists of three steps. In the first step, we use the probability integral transform method to obtain 100 annual set series at monthly scale. The second step, we downscale the synthetic sets from monthly to daily time resolution using a first-order autoregressive model (AR 1). In the last step, we use ND model for generate the 1-min synthetic data sets from daily sets. The algorithm is evaluated at five locations with different type of climate according to the Koppen-Geiger classification and at different temporal scales: annual, monthly, daily, and 1-min resolution. In all cases, synthetic PSYs series cover a wider range of scenarios than the observed series but maintaining their distribution. Results suggest that the synthetically generated PSYs are capable to reproduce the natural variability of the solar resource at any location facilitating the stochastic simulation of solar harnessing systems.