SKIPP'D: A SKy Images and Photovoltaic Power Generation Dataset for short-term solar forecasting. (1st May 2023)

• Record Type: Journal Article
• Authors: Nie, Yuhao; Li, Xiatong; Scott, Andea; Sun, Yuchi; Venugopal, Vignesh; Brandt, Adam
• Is Part Of: Solar energy. Volume 255(2023)
• Page Start: 171
• Page End: 179
• Publication Date: 2023-05-01
• Subjects: Solar forecasting -- PV output prediction -- Fish-eye camera -- Sky images -- Deep learning -- Computer vision; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2023.03.043
• Languages: English
• ISSNs: 0038-092X

Abstract

Abstract: 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. Highlights: A curated sky image and PV generation dataset is released for short-term solar forecasting. Processed benchmark data and raw data are both provided for flexibility of research. Reference codes for data processing and baseline model implementations are provided. Baseline deep learning models are developed to demonstrate the uses of the dataset.