Hourly global solar irradiation forecasting for New Zealand

With the growing use of solar thermal energy systems and small scale photovoltaic power generation by domestic users, there is increasing need to develop intelligent controllers that allow these users to efficiently manage the energy generated by these systems. Ideally these intelligent controllers will be able to forecast the availability and magnitude of the solar resource to plan in advance for periods when the solar irradiance magnitude is small or unavailable. In addition, the method used to provide this forecast needs to be adaptable to a range of timescales and locations. With this in mind, this study examined the possibility of providing a 24-h ahead forecast of hourly global solar irradiation in New Zealand using several approaches but with particular reference to nonlinear autoregressive recurrent neural networks with exogenous inputs (NARX). Hourly time series data for nine historic weather variables recorded over a three year period was used to train and test the forecasting methods for New Zealand's largest city, Auckland. Results from forecasts based on the NARX were compared with an artificial neural network (ANN) based Multilayer Perceptron (MLP) method, a statistical approach using auto regressive moving average (ARMA) and a reference persistence approach. Predicted values of hourly global solar irradiation were compared with the measured values, and it was found that the root mean squared error (RMSE) was 0.243 MI/m(2) for the NARX method as compared to 0.484 MI/m(2), 0.315 MJ/m(2) and 0.514 MJ/m(2) for the MLP, ARMA and persistence approaches respectively. Subsequently the NARX approach was used to forecast global solar irradiation for other major cities across New Zealand. The results demonstrate the ability of the NARX approach to forecast irradiation values at a later time and across a number of different locations. As such it is foreseeable that such an approach could serve as the basis of a forecasting system in future intelligent controllers. (C) 2015 Elsevier Ltd. All rights reserved.