  Large-scale integration of photovoltaics (PV) into electricity grids is
challenged by the intermittent nature of solar power. Sky-image-based solar
forecasting using deep learning has been recognized as a promising approach to
predicting the short-term fluctuations. However, there are few publicly
available standardized benchmark datasets for image-based solar forecasting,
which limits the comparison of different forecasting models and the exploration
of forecasting methods. To fill these gaps, we introduce SKIPP'D -- a SKy
Images and Photovoltaic Power Generation Dataset. The dataset contains three
years (2017-2019) of quality-controlled down-sampled sky images and PV power
generation data that is ready-to-use for short-term solar forecasting using
deep learning. In addition, to support the flexibility in research, we provide
the high resolution, high frequency sky images and PV power generation data as
well as the concurrent sky video footage. We also include a code base
containing data processing scripts and baseline model implementations for
researchers to reproduce our previous work and accelerate their research in
solar forecasting.
