Improving WRF GHI forecasts with model output statistics

Solar energy applications need reliable forecasting of solar irradiance. In this study, we present an assessment of a short-term global horizontal irradiance forecasting system based on Advanced Research Weather Research and Forecasting (WRF-ARW) meteorological model and neural networks as a post-processing method to improve the skill of the system in a highly favorable location for the utilization of solar power in Turkey. The WRF model was used to produce 1 month of 3 days ahead solar irradiance forecasts covering Southeastern Anatolia of Turkey with a horizontal resolution of 4 km. Single-input single-output (SISO) and multi-input single-output (MISO) artificial neural networks (ANN) were used. Furthermore, the overall results of the forecasting system were evaluated by means of statistical indicators: mean bias error, relative mean bias error, root mean square error, and relative root mean square error. The MISO ANN gives better results than the SISO ANN in terms of improving the model predictions, provided by WRF-ARW simulations for August 2011.