PV Fleet Modeling via Smooth Periodic Gaussian Copula
Mehmet G. Ogut Bennet Meyers Stephen P. Boyd
We present a method for jointly modeling power generation from a fleet of
photovoltaic (PV) systems. We propose a white-box method that finds a function
that invertibly maps vector time-series data to independent and identically
distributed standard normal variables. The proposed method, based on a novel
approach for fitting a smooth, periodic copula transform to data, captures many
aspects of the data such as diurnal variation in the distribution of power
output, dependencies among different PV systems, and dependencies across time.
It consists of interpretable steps and is scalable to many systems. The
resulting joint probability model of PV fleet output across systems and time
can be used to generate synthetic data, impute missing data, perform anomaly
detection, and make forecasts. In this paper, we explain the method and
demonstrate these applications.