Forecasting of photovoltaic power generation and model optimization: a review
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Efforts to conserve energy and promote renewable sources have been supported by ML techniques such as LR, ANN, SVM, NC, and EC.
“Efforts to conserve energy and promote renewable sources have been supported by ML techniques such as LR, ANN, SVM, NC, and EC. Researchers and scholars like Akhter et al. [82], Alsadi and Khatib [183], Das et al. [83], Dawoud, Lin, and Okba [184], Khan et al. [84], Youssef, El-Telbany, and Zekry [185], and Wang and Srinivasan [186] have made contributions in this field of study.”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [83]
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 [161]
Since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting and in hybrid physics-plus-ML modeling techniques.
“since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting (Section 8) and in hybrid physics-plus-ML modeling techniques [161,818,822].”
From Tackling climate change with machine learning (2023) · cites this paper as [161]
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Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259 · see citing works
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Tackling climate change with machine learning
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Jamia Millia Islamia (A Central University), & Mateen, S. (2022) · CPSS Transactions on Power Electronics and Applications · doi:10.24295/cpsstpea.2022.00020 · Cited by 42 · see citing works
- Authors
- Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A.
- Affiliations
- University of Malaya (MY)
- Deakin University (AU)
- Swinburne University of Technology (AU)
- Corresponding author
- Tey, Kok Soon
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 81
- Pages
- 912-928
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2751698537
- Cited by
- 1,211
- References
- 130
- Citations by year
- 2026: 128 · 2025: 172 · 2024: 190 · 2023: 183 · 2022: 192 · 2021: 144 · 2020: 119 · 2019: 56 · 2018: 27
- FWCI
- 8.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- Fundamental Research Grant Scheme (FP061-2016)
- University of Malaya (BK052-2016)
- 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 generation forecasting, renewable energy integration, hybrid forecasting model, machine learning forecasting models, statistical forecasting models, data preprocessing, forecast evaluation
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