Solar PV output prediction from video streams using convolutional neural networks
Abstract
To forecast solar power's short-term fluctuation stemming from changes in cloud coverage, a convolutional neural networks (CNN) is used to correlate PV output to contemporaneous sky images.
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Many ML methods have been used to forecast electricity supply and demand.
“To date, many ML methods have been used to forecast electricity supply and demand.”
From Tackling climate change with machine learning (2023) · cites this paper as [771]
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Zhou, Z., Lin, A., He, L., & Wang, L. (2022) · Energies · doi:10.3390/en15093463 · Cited by 13 · see citing works
Xie, Y., Zheng, J., Mei, F., Taylor, G. A., & Gao, A. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125505 · Cited by 12 · see citing works
Detection of clouds in multiple wind velocity fields using ground-based infrared sky images
Terrén-Serrano, G., & Martínez‐Ramón, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110628 · Cited by 12 · see citing works
Gao, Y., Miyata, S., & Akashi, Y. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107814 · Cited by 11 · see citing works
Bayasgalan, O., & Akisawa, A. (2025) · Energies · doi:10.3390/en18092300 · Cited by 9 · see citing works
Kim, S. M., Naqvi, S. D. H., Kang, M. G., Song, H., & Ahn, S. (2022) · Nanomaterials · doi:10.3390/nano12060932 · Cited by 9 · see citing works
Bellido-Jiménez, J. A., Estévez, J., & García-Marín, A. P. (2020) · International Electronic Conference on Atmospheric Sciences (ECAS) · doi:10.3390/ecas2020-08116 · Cited by 9 · see citing works
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting ApplicationsPreprint
Paletta, Q., & Lasenby, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01059 · Cited by 9 · see citing works
IoT and machine learning models for multivariate very short‐term time series solar power forecasting
Kyi, S., & Taparugssanagorn, A. (2024) · IET Wireless Sensor Systems · doi:10.1049/wss2.12088 · Cited by 8 · see citing works
- Authors
- Sun, Y., Szűcs, G., & Brandt, A. R.
- Affiliations
- Stanford University (US)
- Corresponding author
- Brandt, Adam R.
- Venue
- Energy & Environmental Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2018
- Volume
- 11
- Issue
- 7
- Pages
- 1811-1818
- ISSN
- 1754-5692 (ISSN-L)
- 1754-5706
- OpenAlex
- W2802476978
- Cited by
- 169
- References
- 19
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 22 · 2023: 21 · 2022: 25 · 2021: 29 · 2020: 17 · 2019: 12 · 2018: 5
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy & Environmental Science (publishedVersion)
- Funders
- Stanford University (GRSJG)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
- Keywords
- photovoltaic power forecasting, short-term forecasting, cloud cover, sky images, video streams, convolutional neural networks, cloud detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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