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

Yuhao Nie, Xiatong Li, Andea Scott, Yuchi Sun, Vignesh Venugopal +1 more
7/2/2022
cs.CVcs.LG

No official code found. Best implementation: yuhao-nie/Stanford-solar-forecasting-dataset (252 stars, MIT, last commit 2024-09-26).

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.

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Code Implementations(1)

yuhao-nie/Stanford-solar-forecasting-datasetCommunity100%
MIT

Stanford sky images and PV power generation dataset for solar forecasting related research and applications

25242Python, Jupyter NotebookMar 1, 20222 years agoMIT
cloud-detectioncloud-movement-predictioncomputer-visionconvolutional-neural-networksdeep-learning+5 more

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Cite this paper

@article{nie2022skippd,
  title  = {SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting},
  author = {Yuhao Nie and Xiatong Li and Andea Scott and Yuchi Sun and Vignesh Venugopal and Adam Brandt},
  year   = {2022},
  eprint = {2207.00913},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.00913v1}
}

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