Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

Overview

Abstract

The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the daily irradiance time series. When we apply our model to study the statistical properties of observed irradiance data in the Guadeloupe island group, a French overseas region located in the southern Caribbean Sea, we are able to characterize two states of the irradiance series. These states represent the clear-sky and non-clear-sky regimes. Using our model we are able to simulate irradiance series that behave similar to the real data in mean and variability, and more accurate forecasts compared to its competitors. 

Brief Biography

Dr. Carolina Euan is a postdoctoral fellow at the Environmental Statistics Research Group at King Abdullah University of Science and Technology (KAUST). Carolina Euan received her Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in 2016.  She joined KAUST as a Postdoctoral Research Fellow in October 2016. Her research interests are the analysis of High dimensional Time Series, Spatio-Temporal Models, Non-Stationary Processes and Complex Data Visualization.