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  • 标题:Forecasting the Global Horizontal Irradiance based on Boruta Algorithm and Artificial Neural Networks using a Lower Cost
  • 其他标题:Forecasting the Global Horizontal Irradiance based on Boruta Algorithm and Artificial Neural Networks using a Lower Cost
  • 本地全文:下载
  • 作者:Abdulatif Aoihan Alresheedi ; Mohammed Abdullah Al-Hagery
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2020
  • 卷号:11
  • 期号:9
  • DOI:10.14569/IJACSA.2020.0110910
  • 出版社:Science and Information Society (SAI)
  • 摘要:More solar-based electricity generation stations have been established markedly in recent years as new and an important source of renewable energy. That is to ensure a more efficient, reliable integration of solar power to overcome several challenges such as, the future forecasting, the costly equipment in the metrological stations. One of the effective prediction methods is Artificial Neural Networks (ANN) and the Boruta algorithm for optimal attributes selection, to train the proposed prediction model to obtain high accurate prediction performance at a lower cost. The precise goal of this research is to predict the Global Horizontal Irradiance (GHI) by building the ANN model. Also, reducing the total number of GHI prediction attributes/features consequently reducing the cost of devices and equipment required to predict this important factor. The dataset applied in this research is real data, collected from 2015-2018 by solar and meteorological stations in KSA. It provided by King Abdullah City for Atomic and Renewable Energy (KA CARE). The findings emphasize the achievement of accurate predictions of solar radiation with a minimum cost, which is considered to be highly important in KSA and all other countries that have a similar environment.
  • 关键词:Global horizontal irradiance; artificial neural networks; feature selection; boruta algorithm; cost reduction; machine learning
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