Cited by 1,259Solar Radiation and PhotovoltaicsEnergy Load and Power ForecastingPhotovoltaic System Optimization Techniques

A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization

Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.D.·2020·Renewable and Sustainable Energy Reviews

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Crossref · 10.1016/j.rser.2020.109792

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271 of 271 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 [9]

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Authors
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D.
Affiliations
  • The University of Western Australia (AU)
  • Queensland University of Technology (AU)
  • International University of Business Agriculture and Technology (BD)
Corresponding author
Ahmed, R.
Venue
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Published
2020-05
Volume
124
Pages
109792
ISSN
1364-0321 (ISSN-L)
OpenAlex
W3009377873
Cited by
1,259
References
271
Citations by year
2026: 200 · 2025: 254 · 2024: 278 · 2023: 193 · 2022: 192 · 2021: 113 · 2020: 29
FWCI
51.80
Type
journal article
Language
en
Open access
closed
Locations
  • Renewable and Sustainable Energy Reviews (publishedVersion)
  • UWA Profiles and Research Repository (UWA) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
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, solar energy integration, solar irradiance, weather classification, cloud motion, data preprocessing, wavelet transform, generative adversarial networks, uncertainty quantification, hybrid neural network, ensemble forecasting, online sequential extreme learning machine, convolutional neural networks, genetic algorithm optimization, particle swarm optimization, forecast evaluation, short-term forecasting

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