Irradiance Forecasting Using the Local Ensemble Transform Kalman Filter, Satellite Images, and Ground Sensors

Satellite images provide a basis for estimating global horizontal irradiance over areas on the scale of a city or larger. In this work, we aim to improve satellite derived estimates by combining them with ground sensor data and an advection model in a Bayesian framework to produce both improved estimates and forecasts.

We use a data assimilation technique known as the Local Ensemble Transform Kalman Filter (LETKF). The LETKF is a square root filter in which calculations are performed in the space spanned by ensemble members, a lower dimensional subspace of the state space. This allows for a reduction in computational complexity because the number of ensemble members (around 50) is significantly lower than the dimension of the state space (hundreds of thousands).

Within this framework, we utilize satellite images taken from the GOES-15 geostationary satellite (available every 15-30 minutes) as well as ground data taken from irradiance sensors and rooftop solar arrays (available every 5 minutes). We use an advection model, driven by wind forecasts from a numerical weather model, which simulates cloud motion. This model is then used to span the time between measurements as well as to create forecasts.

We present preliminary results showing the effectiveness of this method to produce irradiance estimates, forecasts, and uncertainty quantification. We also study how the accuracy of forecasts and uncertainty projections are affected by the localization and inflation in the LETKF, cloud motion uncertainty, and random cloud structure perturbations. Benefits and drawbacks of the present framework as well as future improvements are also discussed.