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- Preferred citation
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Nie, Y., Li, X., Scott, A., Sun, Y., Venugopal, V., and Brandt, A. (2022). 2017-2019 Sky Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting (Stanford Benchmark). Stanford Digital Repository. Available at https://purl.stanford.edu/dj417rh1007
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Nie, Y., Li, X., Scott, A., Sun, Y., Venugopal, V., and Brandt, A. (2022). 2017 Sky Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting (Stanford Raw). Stanford Digital Repository. Available at https://purl.stanford.edu/sm043zf7254
- Preferred citation
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Nie, Y., Li, X., Scott, A., Sun, Y., Venugopal, V., and Brandt, A. (2022). 2019 Sky Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting (Stanford Raw). Stanford Digital Repository. Available at https://purl.stanford.edu/jj716hx9049
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SUNSET Model GitHub Repo
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Stanford University Sky Images and PV Power Generation Dataset GitHub Repo
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Sun, Y., Szűcs, G., Brandt, A.R., 2018. Solar PV output prediction from video streams using convolutional neural networks. Energy Environ. Sci. 11, 1811–1818. https://doi.org/10.1039/C7EE03420B
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Sun, Y., Venugopal, V., Brandt, A.R., 2019. Short-term solar power forecast with deep learning: Exploring optimal input and output configuration. Sol. Energy 188, 730–741. https://doi.org/10.1016/j.solener.2019.06.041
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Venugopal, V., Sun, Y., Brandt, A.R., 2019. Short-term solar PV forecasting using computer vision: The search for optimal CNN architectures for incorporating sky images and PV generation history. J. Renew. Sustain. Energy 11, 066102. https://doi.org/10.1063/1.5122796
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Sun, Y., 2019. Short-term Solar Forecast Using Convolutional Neural Networks with Sky Images. Stanford University.Venugopal, V., 2019. Search for Optimal CNN Architectures Incorporating Heterogeneous Inputs for Short- term Solar PV Forecasting. Stanford University.
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Nie, Y., Sun, Y., Chen, Y., Orsini, R., Brandt, A., 2020. PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model. J. Renew. Sustain. Energy 12, 046101. https://doi.org/10.1063/5.0014016
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Nie, Y., Zamzam, A.S., Brandt, A., 2021. Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Sol. Energy 224, 341–354. https://doi.org/10.1016/j.solener.2021.05.095
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