Short-term PV output prediction using convolutional neural network: learning from an imbalanced sky images dataset via sampling and data augmentation (Papers Track)

Yuhao Nie (Stanford University); Ahmed S Zamzam (The National Renewable Energy Laboratory); Adam Brandt (Stanford University)

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Abstract

Integrating photovoltaics (PV) into electricity grids is one of the major pathways towards a low-carbon energy system. However, the biggest challenge is the strong fluctuation in PV power generation. In recent years, sky-image-based PV output prediction using deep neural networks has emerged as a promising approach to alleviate the uncertainty. Despite the research surge in exploring different model architectures, there is currently no study addressing the issue of learning from an imbalanced sky images dataset, the outcome of which would be highly beneficial for improving the reliability of existing and new solar forecasting models. In this study, we train convolutional neural network (CNN) models from an imbalanced sky images dataset for two disparate PV output prediction tasks, i.e., nowcast and forecast. We empirically examine the efficacy of using different sampling and data augmentation approaches to create synthesized dataset for model development. We further apply a three-stage selection approach to determine the optimal sampling approach, data augmentation technique and oversampling rate.

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