Streamline-based method for intra-day solar forecasting through remote sensing

• Deterministic remote sensing based intra-day forecasting model.
• Input variables are solely satellite images and ground measurements.
• Ground truth improved satellite-to-irradiance model for preprocessed images available near real-time.
• Optical flow based cloud tracking.
• Streamline calculation for the identification of advecting cloud fields.

This work presents an enhanced deterministic solar irradiance forecasting approach that relies on satellite images and ground measurements as inputs. The proposed method is based on a ground-truth improved satellite-to-irradiance model for the prediction of global horizontal irradiance (GHIGHI). This approach relies on cloud tracking and advection with an optical flow algorithm. The application of the optical flow algorithm between two consecutive satellite image frames allows for the calculation of a vector field covering each pixel in the satellite image. This cloud motion vector (CMVCMV) field determines the streamline passing through the location of interest. The estimated cloud advection along the quasi-steady streamline to the location of interest is than computed, and this information is translated into an irradiance forecast for the location of interest. In order to reduce the error associated with a linear satellite-to-irradiance model, a novel approach employing ground measurements is proposed. Additionally, decision heuristics are identified and implemented to issue a forecast based on CMVCMV or persistence, depending on the current sky conditions. The overall method is tested for over 110 days of operational 1-, 2- and 3-h ahead GHIGHI forecasts, implemented and evaluated for San Diego, California. The continual forecasting skill of this method for the 110 days ranges between 8% and 19% over persistence, depending on the forecast horizon. While previously proposed methods achieve similar skills, the completely deterministic approach combined with comparably low computation and implementation costs makes the proposed method suitable for applications with limited availability of data.