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A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
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“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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Metadata
- 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
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › 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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