Estimation of visibility from spectral irradiance using artificial neural networks

Abstract

Visibility has become a key input to determine the transmission losses of solar radiation propagating between heliostats and the receiver of solar tower power (STP) plants. Recent studies suggest that haze can reduce visibility and increase these losses up to 25% compared to clear conditions. Monitoring visibility would thus be needed for proper design and operation of STPs, but this is usually not done at all potential sites. In this work, the dependence of visibility's magnitude on relative humidity (RH) and aerosol optical depth (AOD) at three different wavelengths is analyzed. To that effect, 1-min observations from a visibilimeter located in Huelva (southwestern Spain) are analyzed during the winter season. RH is linearly correlated with visibility and explains 46% of its variability. A complex non-linear relationship between visibility and AOD is observed with also dependence on RH. Artificial neural networks (ANN) are thus investigated here for mapping the complex and non-linear relationships between visibility, RH and AOD at multiple wavelengths. This improves results significantly, increasing the explained visibility variability up to 68% and reducing RMSD from 30% to 22% with almost zero bias. The ANN analysis shows that the visibility-AOD relationship is not sensitive to the specific wavelength at which AOD is measured. These findings show that, using ANN, visibility can be estimated from local observations of RH and AOD at only a single wavelength.