Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions

Hayajneh, A.M., Alasali, F., Salama, A. orcid.org/0000-0002-3339-8292 et al. (1 more author) (2024) Intelligent Solar Forecasts: Modern Machine Learning Models and TinyML Role for Improved Solar Energy Yield Predictions. IEEE Access, 12. pp. 10846-10864. ISSN: 2169-3536

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Item Type: Article
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© 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC-ND 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Solar power forecasting, time series forecasting, Internet of Things, deep neural networks
Dates:
  • Accepted: 11 January 2024
  • Published (online): 15 January 2024
  • Published: 24 January 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds)
Date Deposited: 01 Oct 2025 15:34
Last Modified: 01 Oct 2025 15:34
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: 10.1109/access.2024.3354703
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 7: Affordable and Clean Energy
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