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---
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}
}
``` |