Abstract :
We develop and validate a medium-term solar irradiance forecasting model by adopting predicted meteorological variables from the
US National Weather Service’s (NWS) forecasting database as inputs to an Artificial Neural Network (ANN) model. Since the inputs
involved are the same as the ones available from a recently validated forecasting model, we include mean bias error (MBE), root mean
square error (RMSE), and correlation coefficient (R2) comparisons between the more established forecasting model and the proposed
ones. An important component of our study is the development of a set of criteria for selecting relevant inputs. The input variables
are selected using a version of the Gamma test combined with a genetic algorithm. The solar geotemporal variables are found to be critically
important, while the most relevant meteorological variables include sky cover, probability of precipitation, and maximum and minimum
temperatures. Using the relevant input sets identified by the Gamma test, the developed forecasting models improve RMSEs for
GHI by 10–15% over the reference model. Prediction intervals based on regression of the squared residuals on the input variables are also
derived.
2011 Elsevier Ltd. All rights reserved
