Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks

Abstract

We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote sensing images are then processed using the Spectral Cloud Optical Property Estimation (SCOPE) method in conjunction with Convolution Neural Networks (CNNs) for nowcasts and 1-hour ahead forecasts. We propose and compare two CNN based models for GHI now-casting with a 1-hour forecast horizon and a time resolution that processes data every 5 minutes: one enhanced with the SCOPE method for cloud estimation and another without it. The inclusion of SCOPE-derived information significantly improves model performance, yielding an average RMSE of 44.7 versus 68.9 W/m 2 for the model without SCOPE information. The present work underscores the efficacy of even basic CNNs in interpreting satellite imagery combined with atmospheric models of the atmosphere to estimate ground irradiance accurately. The hybrid SCOPE-CNN model outperforms the basic CNN model that relies solely on 10 longwave channels, indicating the relevance of SCOPE’s physical features in enhancing predictions across various solar micro-climates. Further advancements using Convolutional Long Short-Time Memory (ConvLSTM) schemes lead to the development of a SCOPE-CNN-ConvLSTM model, showcasing significant enhancements over smart persistence across all conditions. • Remote sensing images are combined with CNNs to produce high-fidelity solar forecasts. • A hybridized physical model of cloud layers increases the forecasting skills. • A Long Short-Term Memory CNN model is applied to further improve solar forecasts. • Remote sensing plus CNNs offer novel ways to forecast solar irradiance. • Hybrid short-term forecasts proposed here outperform existing methods

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