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Artificial intelligence techniques for sizing photovoltaic systems: A review

Author

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  • Mellit, A.
  • Kalogirou, S.A.
  • Hontoria, L.
  • Shaari, S.

Abstract

Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. AI-techniques have the following features: can learn from examples; are fault tolerant in the sense that they are able to handle noisy and incomplete data; are able to deal with non-linear problems; and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a myriad of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI have been used and applied in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of the AI-techniques for sizing photovoltaic (PV) systems: stand-alone PVs, grid-connected PV systems, PV-wind hybrid systems, etc. Published literature presented in this paper show the potential of AI as a design tool for the optimal sizing of PV systems. Additionally, the advantage of using an AI-based sizing of PV systems is that it provides good optimization, especially in isolated areas, where the weather data are not always available.

Suggested Citation

  • Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
    • Handle: RePEc:eee:rensus:v:13:y:2009:i:2:p:406-419
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    References listed on IDEAS

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    1. Hernández, J.C. & Medina, A. & Jurado, F., 2007. "Optimal allocation and sizing for profitability and voltage enhancement of PV systems on feeders," Renewable Energy, Elsevier, vol. 32(10), pages 1768-1789.
    2. Mellit, A. & Benghanem, M. & Arab, A. Hadj & Guessoum, A., 2005. "An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria," Renewable Energy, Elsevier, vol. 30(10), pages 1501-1524.
    3. Senjyu, Tomonobu & Hayashi, Daisuke & Yona, Atsushi & Urasaki, Naomitsu & Funabashi, Toshihisa, 2007. "Optimal configuration of power generating systems in isolated island with renewable energy," Renewable Energy, Elsevier, vol. 32(11), pages 1917-1933.
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    5. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Contreras, Javier, 2007. "Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage," Renewable Energy, Elsevier, vol. 32(7), pages 1102-1126.
    6. El-Hefnawi, Said H., 1998. "Photovoltaic diesel-generator hybrid power system sizing," Renewable Energy, Elsevier, vol. 13(1), pages 33-40.
    7. Sukamongkol, Y. & Chungpaibulpatana, S. & Ongsakul, W., 2002. "A simulation model for predicting the performance of a solar photovoltaic system with alternating current loads," Renewable Energy, Elsevier, vol. 27(2), pages 237-258.
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