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Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons

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  • Pang, Zhihong
  • Niu, Fuxin
  • O’Neill, Zheng

Abstract

With the rapid advancement of the high-performance computing technology and the increasing availability of the mass-storage memory device, the application of the data-driven models (e.g., the artificial neural network (ANN) model) for solar radiation prediction is appearing in abundance in the past decade. Although the performances of these models have been discussed in a large number of studies, how to further enhance the forecasting accuracies of these data-driven approaches to better facilitate the advanced controls in the building system such as model predictive control (MPC) in smart buildings remains a challenge. Deep learning, which is considered as a powerful tool to move machine learning closer to one of its original goals, i.e., Artificial Intelligence (AI), is a viable solution to this problem.

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  • Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    • Handle: RePEc:eee:renene:v:156:y:2020:i:c:p:279-289
    DOI: 10.1016/j.renene.2020.04.042
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    References listed on IDEAS

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