Convolutional neural networks for intra-hour solar forecasting based on sky image sequences.

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%–39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 × 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed. • Two end-to-end sky image-based CNNs are proposed for operational solar forecasting. • Experiments with six years of open-source data show the outperformance of 20%–39%. • Sensitivity analyses are performed to give practical suggestions. • Visualization interpretation analyses are performed to interpret the learning process. [ABSTRACT FROM AUTHOR]