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Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

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  • Olivencia Polo, Fernando A.
  • Ferrero Bermejo, Jesús
  • Gómez Fernández, Juan F.
  • Crespo Márquez, Adolfo

Abstract

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time.

Suggested Citation

  • Olivencia Polo, Fernando A. & Ferrero Bermejo, Jesús & Gómez Fernández, Juan F. & Crespo Márquez, Adolfo, 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models," Renewable Energy, Elsevier, vol. 81(C), pages 227-238.
    • Handle: RePEc:eee:renene:v:81:y:2015:i:c:p:227-238
    DOI: 10.1016/j.renene.2015.03.023
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    Cited by:

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    3. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    4. Hussain, Muhammed & Dhimish, Mahmoud & Titarenko, Sofya & Mather, Peter, 2020. "Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters," Renewable Energy, Elsevier, vol. 155(C), pages 1272-1292.
    5. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    6. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    7. Sergey Zhironkin & Elena Dotsenko, 2023. "Review of Transition from Mining 4.0 to 5.0 in Fossil Energy Sources Production," Energies, MDPI, vol. 16(15), pages 1-35, August.
    8. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
    9. Wang, Jing-Yi & Qian, Zheng & Zareipour, Hamidreza & Wood, David, 2018. "Performance assessment of photovoltaic modules based on daily energy generation estimation," Energy, Elsevier, vol. 165(PB), pages 1160-1172.
    10. Néstor Rodríguez-Padial & Marta Marín & Rosario Domingo, 2017. "An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning," Complexity, Hindawi, vol. 2017, pages 1-15, October.
    11. Milad Bagheri & Zelina Z. Ibrahim & Mohd Fadzil Akhir & Bahareh Oryani & Shahabaldin Rezania & Isabelle D. Wolf & Amin Beiranvand Pour & Wan Izatul Asma Wan Talaat, 2021. "Impacts of Future Sea-Level Rise under Global Warming Assessed from Tide Gauge Records: A Case Study of the East Coast Economic Region of Peninsular Malaysia," Land, MDPI, vol. 10(12), pages 1-24, December.
    12. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    13. Orlando Durán & Paulo Sergio Afonso & Paulo Andrés Durán, 2019. "Spare Parts Cost Management for Long-Term Economic Sustainability: Using Fuzzy Activity Based LCC," Sustainability, MDPI, vol. 11(7), pages 1-14, March.
    14. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    15. Choi, Jongwoo & Lee, Il-Woo & Cha, Suk-Won, 2022. "Analysis of data errors in the solar photovoltaic monitoring system database: An overview of nationwide power plants in Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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