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New approach for solar tracking systems based on computer vision, low cost hardware and deep learning

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  • Carballo, Jose A.
  • Bonilla, Javier
  • Berenguel, Manuel
  • Fernández-Reche, Jesús
  • García, Ginés

Abstract

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almería (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.

Suggested Citation

  • Carballo, Jose A. & Bonilla, Javier & Berenguel, Manuel & Fernández-Reche, Jesús & García, Ginés, 2019. "New approach for solar tracking systems based on computer vision, low cost hardware and deep learning," Renewable Energy, Elsevier, vol. 133(C), pages 1158-1166.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:1158-1166
    DOI: 10.1016/j.renene.2018.08.101
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    References listed on IDEAS

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    Cited by:

    1. Satué, Manuel G. & Castaño, Fernando & Ortega, Manuel G. & Rubio, Francisco R., 2020. "Power feedback strategy based on efficiency trajectory analysis for HCPV sun tracking," Renewable Energy, Elsevier, vol. 161(C), pages 65-76.
    2. Sun, Leihou & Bai, Jianbo & Pachauri, Rupendra Kumar & Wang, Shitao, 2024. "A horizontal single-axis tracking bracket with an adjustable tilt angle and its adaptive real-time tracking system for bifacial PV modules," Renewable Energy, Elsevier, vol. 221(C).
    3. Sridharan Naveen Venkatesh & Vaithiyanathan Sugumaran, 2022. "A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules," Journal of Risk and Reliability, , vol. 236(1), pages 148-159, February.
    4. Max Pargmann & Jan Ebert & Markus Götz & Daniel Maldonado Quinto & Robert Pitz-Paal & Stefan Kesselheim, 2024. "Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).

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