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Large-scale integration of photovoltaics (PV) into electricity grids ischallenged by the intermittent nature of solar power. Sky-image-based solarforecasting using deep learning has been recognized as a promising approach topredicting the short-term fluctuations. However, there are few publiclyavailable standardized benchmark datasets for image-based solar forecasting,which limits the comparison of different forecasting models and the explorationof forecasting methods. To fill these gaps, we introduce SKIPP'D -- a SKyImages and Photovoltaic Power Generation Dataset. The dataset contains threeyears (2017-2019) of quality-controlled down-sampled sky images and PV powergeneration data that is ready-to-use for short-term solar forecasting usingdeep learning. In addition, to support the flexibility in research, we providethe high resolution, high frequency sky images and PV power generation data aswell as the concurrent sky video footage. We also include a code basecontaining data processing scripts and baseline model implementations forresearchers to reproduce our previous work and accelerate their research insolar forecasting.

SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting