Cited by 169Solar Radiation and PhotovoltaicsEnergy Load and Power ForecastingPhotovoltaic System Optimization Techniques

Solar PV output prediction from video streams using convolutional neural networks

Sun, Yuchi, Szűcs, Gergely, Brandt, Adam R.·2018·Energy & Environmental Science

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.

Integrity signals

Registry

Crossref · 10.1039/c7ee03420b

Cites

18 of 18 references verified0 citation errors · 0 not checkable

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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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Authors
Sun, Y., Szűcs, G., & Brandt, A. R.
Affiliations
Stanford University (US)
Corresponding author
Brandt, Adam R.
Venue
Energy & Environmental Science
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

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