SkyCam: A Dataset of Sky Images and their Irradiance values
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Abstract (arXiv)
Recent advances in Computer Vision and Deep Learning have enabled astonishing results in a variety of fields and applications. Motivated by this success, the SkyCam Dataset aims to enable image-based Deep Learning solutions for short-term, precise prediction of solar radiation on a local level. For the span of a year, three different cameras in three topographically different locations in Switzerland are acquiring images of the sky every 10 seconds. Thirteen high resolution images with different exposure times are captured and used to create an additional HDR image. The images are paired with highly precise irradiance values gathered from a high-accuracy pyranometer.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 0 of its 6 references that are in the map. First posted on arXiv 6 May 2021.
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Citing papers per year: 2021: 1; 2022: 3.
4 papers.
- 2022SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecastingcited by 11
- 2021SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecastingcited by 7
- 2022Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?cited by 2
- 2022Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Surveycited by 2
- 2022Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey27 Nov 2022
- 2022Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?3 Nov 2022
- 2022SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting2 Jul 2022
- 2021SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting29 Nov 2021