Forecasting Daily Solar Energy Production Using Robust Regression Techniques

We describe a novel approach to forecast daily solar energy production based on the output of a numerical weather prediction (NWP) model using non-parametric robust regression techniques. Our approach comprises two steps: First, we use a non-linear interpolation technique, Gaussian Process regression (also known as Kriging in Geostatistics), to interpolate the coarse NWP grid to the location of the solar energy production facilities. Second, we use Gradient Boosted Regression Trees, a non-parametric regression technique, to predict the daily solar energy output based on the interpolated NWP model and additional spatio-temporal features.

Experimental evidence suggests that two aspects of our approach are crucial for its effectiveness: a) the ability of Gaussian Process regression to incorporate both input and output uncertainty which we leverage by deriving input uncertainty from an ensemble of 11 NWP models and including convidence intervals alongside the interpolated point estimates and b) the ability of Gradient Boosted Regression Trees to handle outliers in the outputs by using robust loss functions - a property that is very important due to the volatile nature of solar energy output.

We evaluated the approach on a dataset of daily solar energy measurements from 98 stations in Oklahoma. The results show a relative improvement of 17.17% and 46.19% over the baselines, Spline Interpolation and Gaussian Mixture Models, resp.