Forecasting of One-Day-Ahead Global Horizontal Irradiation Using Block-Oriented Models Combined with a Swarm Intelligence Approach

Abstract

Solar photovoltaic (PV) energy is becoming used gradually as an alternative source to classical fossil fuel because of being renewable, clean and abundant. In agricultural regions such as Hail, Saudi Arabia, farmers can invest in using PV sources for pumping water from relatively deep wells. In such case, forecasting the potential of PV energy is among the primary factors that may affect the design of a PV water pumping system efficiency and reliability. In this paper, a novel combined method based on Hammerstein—autoregressive with exogenous input—Wiener model optimized by particle swarm optimization was developed. Four models including, saturation, dead-zone and polynomial nonlinear blocks have been evaluated using global horizontal irradiation (GHI) as model output and separately, the temperature, the clearness index, the relative humidity and the wind speed as model inputs on daily basis collected from Hail region, Saudi Arabia over 20 years (2000–2019). The collected 7305 observations were divided into a training subset (6205) and a testing subset (1095). Six other models including persistence forecast, smart persistence forecast, time series autoregressive and artificial neural networks have been also implemented on the same dataset for comparison purpose. Based on robust performance indicators (mean absolute percentage error, coefficient of determination, the root mean square error and skill score), the Hammerstein–Saturation with temperature as input model outperformed all the other developed models. To validate the suitability of the proposed approach for forecasting GHI in arid climates, our best model was implemented on two locations picked randomly in the Hail region (Saudi Arabia). The results of validation were in the same scale of accuracy as the primary best model.