Short–term global solar radiation forecasting based on an improved method for sunshine duration prediction and public weather forecasts

Abstract

Accurate forecasting of daily global solar radiation (Rs) is important for photovoltaic power and other sectors. Numerical models coupled with public weather forecasts information is a feasible method to predict short–term daily Rs. Here, we propose a novel sunshine duration converting method (n_new) based on forecasted air temperature and weather types data, which we validated using measurements from 86 radiation stations. A widely-used, generalized sunshine–based Rs model (Rs_n) was then coupled with the n_new method (Rs_n new) for forecasting daily Rs. This was further compared to Rs_n incorporated with the common sunshine duration converting method (n_com) using only weather types data (Rs_n com) and a recently developed generalized temperature–based model (Rs_T). The results indicated that the n_new method produced better estimates than the n_com method, as indicated by increased mean correlation coefficient (R; 13.0%–24.5%) and index of agreement (dIA; 2.9%–9.5%) and decreased mean root mean squared error (RMSE; 12.8%–14.8%) for the 1–7 days lead time over 86 sites. The Rs_n new model improved the accuracy for 98% of sites when compared to the Rs_n com model, with mean values of R and dIA increasing by 7.7%–11.0% and 2.1%–4.8% and that of RMSE decreasing by 9.7%–12.5% for the 1–7 days lead time. The results suggest that the Rs_n new model is advantageous in short–term forecasts. The Rs_n new model ranked first for 52.3%–74.4% of sites for the 1–7 days lead time, followed by the Rs_T model (25.6%–47.7%). Moreover, there was generally a better performance for the Rs_n new model to forecast daily Rs at a longer lead time. Therefore, the Rs_n new model using weather forecasts information is highly recommended to forecast short–term daily Rs.