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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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    Cited by:

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    8. Dai, Yeming & Wang, Yanxin & Leng, Mingming & Yang, Xinyu & Zhou, Qiong, 2022. "LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method," Energy, Elsevier, vol. 256(C).
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    11. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
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