Random Elastic Space-Time (REST) Prediction and Solar Irradiance Studies

Music City Center 

As the power grid moves to a more renewable future, energy sources from weather-driven phenomena such as solar power will form an increasingly large portion of electricity generation.  The variability, non-Gaussianity and intermittency of solar resources challenge current grid operation paradigms, and realistic data scenarios are required for grid planning and operational studies.  However, such data are not available at the space-time resolution needed for realistic grid models.  Given sparse spatial samples, we introduce a framework for spatiotemporal prediction in a functional data analysis framework when data exhibit nonstationary phase misalignment.  The approach is illustrated on a challenging high-frequency irradiance dataset and compared with existing methods.

curve registration


distributed photovoltaic systems

functional data analysis

spatiotemporal prediction 

Section on Statistics and the Environment