SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting
This dataset addresses a gap for researchers in solar forecasting by providing a standardized resource to compare models and explore methods, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of publicly available standardized benchmark datasets for sky-image-based solar forecasting by introducing SKIPP'D, a dataset containing three years of quality-controlled sky images and photovoltaic power generation data, along with tools like baseline models and processing scripts to facilitate research.
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.