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Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects

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  • Gandoman, Foad H.
  • Abdel Aleem, Shady H.E.
  • Omar, Noshin
  • Ahmadi, Abdollah
  • Alenezi, Faisal Q.

Abstract

The use of solar photovoltaic (PV) systems as green renewable sources for electricity generation in modern power networks is steadily increasing. One of the problems with using PV units in developing countries and small companies is access to a simple model to assess the short-time output of a solar cell. For example, a robust model that is capable of analyzing cloud variations and ambient temperature (important factors impacting the PV output) to assess short-term PV output would be very helpful. This article proposes a new methodology to assess the impacts of these factors on the hourly output power of a PV system. Our results showed that the proposed model has the ability to assess PV output by using the hourly data of cloud and ambient temperature change. To validate the model, the output was compared to results of measurements from PV systems installed in Sanandaj and Rasht cities located in North-West (latitude 35.31 and longitude 47.00) and North (latitude 37.28 and longitude 49.58) Iran, respectively. Also, the standardized root-mean-square error method (nRMSE) was used to validate the high accuracy of the proposed method.

Suggested Citation

  • Gandoman, Foad H. & Abdel Aleem, Shady H.E. & Omar, Noshin & Ahmadi, Abdollah & Alenezi, Faisal Q., 2018. "Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects," Renewable Energy, Elsevier, vol. 123(C), pages 793-805.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:793-805
    DOI: 10.1016/j.renene.2018.02.102
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    1. Sabziparvar, Ali A. & Shetaee, H., 2007. "Estimation of global solar radiation in arid and semi-arid climates of East and West Iran," Energy, Elsevier, vol. 32(5), pages 649-655.
    2. Gandoman, Foad H. & Raeisi, Fatima & Ahmadi, Abdollah, 2016. "A literature review on estimating of PV-array hourly power under cloudy weather conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 579-592.
    3. Albizzati, Enrique D. & Rossetti, Germán H. & Alfano, Orlando M., 1997. "Measurements and predictions of solar radiation incident on horizontal surfaces at Santa Fe, Argentina (31° 39′S, 60° 43′W)," Renewable Energy, Elsevier, vol. 11(4), pages 469-478.
    4. Perpiña Castillo, Carolina & Batista e Silva, Filipe & Lavalle, Carlo, 2016. "An assessment of the regional potential for solar power generation in EU-28," Energy Policy, Elsevier, vol. 88(C), pages 86-99.
    5. Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
    6. Pérez-Higueras, P.J. & Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Hontoria, L., 2012. "A simplified method for estimating direct normal solar irradiation from global horizontal irradiation useful for CPV applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5529-5534.
    7. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    8. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
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    11. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
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