V.
Srikrishnan, G.S. Young, L. Witmer, J.R.S. Brownson,
2015
Using
Multi-Pyranometer Arrays and Neural Networks to Estimate Direct Normal
Irradiance
Solar
Energy. 119, 531-542
Abstract
Direct Normal Irradiance (DNI) is a critical component of solar irradiation
for estimating Plane of Array (POA) irradiance on flat plate systems and for
estimating photovoltaic and concentrating system power output. Current
approaches to measuring or estimating DNI suffer from either high equipment
costs or low precision and may require detailed environmental data. An
alternative approach, using artificial neural networks to estimate DNI from the
irradiance measurements of multiple pyranometers, is studied. We consider
various neural network topologies and study the resulting errors. The neural
network-based estimators are found to have higher accuracy than those obtained
from empirical correlations of GHI measurements alone. Additionally, the use of
a different GHI sensor than the one used to obtain the neural network training
data does not induce significant errors. The ability of this method to be used
as a quality-control instrument for pyrheliometer measurements is also
discussed. We find that the proposed methodology is capable of detecting many
instances of unreliable DNI measurements by considering the deviation between
the predicted DNI and measured DNI. A more detailed analysis can be conducted
by taking advantage of the data streams from the individual pyranometers.