Real-time Clear-sky Model and Cloud Cover for Direct Normal Irradiance Prediction

Abstract

Direct normal irradiance (DNI) prediction is of great significance for the concentrated solar power (CSP) generation and grid work. The clouds are the main cause of DNI intensity reduction and drastic changes. Therefore, the real-time clear-sky DNI model coupled with cloud cover is proposed for short-term DNI prediction in this study. The Linke turbidity coefficient is used to develop a clear-sky model, and real-time adjustment of the coefficients is achieved by identifying clear-sky period. Then, the theoretical clear-sky value of current day can be obtained. By the combination of the historical cloud cover and theoretical clear-sky value, Auto Regressive Moving Average (ARMA) and Artificial Neural Network (ANN) are used to develop linear and nonlinear models. Experiment with the data in the National Renewable Energy Laboratory (NREL) database, the simulation results show that this approach using cloud cover and clear-sky information can improve the forecasting accuracy.