Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs

• We create forecasting models for global horizontal irradiance using satellite images and artificial neural networks.
• We present an algorithm to compute mean cloud velocity from satellite images.
• We present an algorithm to estimate the influence of cloud cover in the short-term forecast of global horizontal irradiance.
• We compare the hybrid approaches using standard and advanced metrics to quantify forecasting skills
• The developed models are as good or better than conventional frozen cloud translation approaches

This work describes a new hybrid method that combines information from processed satellite images with Artificial Neural Networks (ANNs) for predicting global horizontal irradiance (GHI) at temporal horizons of 30, 60, 90, and 120 min. The forecast model is applied to GHI data gathered from two distinct locations (Davis and Merced) that represent well the geographical distribution of solar irradiance in the San Joaquin Valley. The forecasting approach uses information gathered from satellite image analysis including velocimetry and cloud indexing as inputs to the ANN models. To the knowledge of the authors, this is the first attempt to hybridize stochastic learning and image processing approaches for solar irradiance forecasting. We compare the hybrid approaches using standard error metrics to quantify the forecasting skill for the several time horizons considered.