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

arXiv: https://arxiv.org/abs/2207.00913 · HTML: https://mlsanity.com/paper/2207.00913/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 2 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Photovoltaic Nowcasting](https://mlsanity.com/paper/1810.06327/) (2018)
- [Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis](https://mlsanity.com/paper/2102.00721/) (2021)
- [ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy](https://mlsanity.com/paper/2104.12419/) (2021)
- [Girasol, a Sky Imaging and Global Solar Irradiance Dataset](https://mlsanity.com/paper/2102.07814/) (2021)
- [SkyCam: A Dataset of Sky Images and their Irradiance values](https://mlsanity.com/paper/2105.02922/) (2021)

## Built on by (most cited first, 11 of 11)

- [LightLLM: A Versatile Large Language Model for Predictive Light Sensing](https://mlsanity.com/paper/2411.15211/) (2024)
- [Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning](https://mlsanity.com/paper/2410.03390/) (2024)
- [Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?](https://mlsanity.com/paper/2211.02108/) (2022)
- [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://mlsanity.com/paper/2211.14709/) (2022)
- [SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT](https://mlsanity.com/paper/2306.11682/) (2023)
- [SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models](https://mlsanity.com/paper/2502.10307/) (2025)
- [Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting](https://mlsanity.com/paper/2605.19074/) (2026)
- [SolarBench: A global solar energy nowcasting benchmark](https://mlsanity.com/paper/2609.06187/) (2026)
- [Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models](https://mlsanity.com/paper/2609.11135/) (2026)
- [PhyMo: A Physical-Field Modality for Multimodal AI4Physics](https://mlsanity.com/paper/2609.27554/) (2026)
- [ViBR-WM: Visual Bayesian Regression for World Modeling](https://mlsanity.com/paper/2609.33844/) (2026)

## Abstract (arXiv)

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.