---
license: cc-by-4.0
dataset_info:
- config_name: default
  features:
  - name: image
    dtype: image
  - name: time
    dtype: timestamp[s, tz=US/Pacific]
  - name: pv
    dtype: float32
  splits:
  - name: train
    num_bytes: 2228797291.64
    num_examples: 349372
  - name: test
    num_bytes: 90193889.61
    num_examples: 14003
  download_size: 2321743541
  dataset_size: 2318991181.25
- config_name: labels
  features:
  - name: time
    dtype: timestamp[s, tz=US/Pacific]
  - name: pv
    dtype: float32
  splits:
  - name: train
    num_bytes: 4192464
    num_examples: 349372
  - name: test
    num_bytes: 168036
    num_examples: 14003
  download_size: 4660741
  dataset_size: 4360500
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
- config_name: labels
  data_files:
  - split: train
    path: labels/train-*
  - split: test
    path: labels/test-*
---

## Citation
If you find SKIPP'D useful to your research, please cite:
```
Nie, Y., Li, X., Scott, A., Sun, Y., Venugopal, V., & Brandt, A. (2023). SKIPP’D: A SKy Images and Photovoltaic Power Generation Dataset for short-term solar forecasting. Solar Energy, 255, 171-179.
```
or
```
@article{nie2023skipp,
  title={SKIPP’D: A SKy Images and Photovoltaic Power Generation Dataset for short-term solar forecasting},
  author={Nie, Yuhao and Li, Xiatong and Scott, Andea and Sun, Yuchi and Venugopal, Vignesh and Brandt, Adam},
  journal={Solar Energy},
  volume={255},
  pages={171--179},
  year={2023},
  publisher={Elsevier}
}
```