2023 Vol.6 Dec.N06

• Title: Solar power prediction algorithm based on improved BP neural network
• Name: Yang Liu
• Company: College of Computer Science and Technology,Inner Mongolia Normal University,Inner Mongolia Huhot 010022,China
• Abstract:

  The current conventional solar power generation power prediction algorithm mainly constructs a nonlinear prediction model by directly processing the data from the photovoltaic power plant, which results in poor prediction accuracy due to the lack of dimensionality reduction processing of the data. In this regard, the solar power generation power prediction algorithm based on improved BP neural network is proposed. Firstly, box plots are used to eliminate the abnormal data, and normalization as well as correlation analysis are processed. The Pearson correlation coefficient method is used to reduce the dimensionality of the power features, and finally the relevant parameters of the algorithm are selected to construct the prediction process. The final comparison results prove that when the proposed method is used to predict the power of solar power generation, the fitting degree between the prediction results and the actual results is high, and the prediction accuracy is more ideal.

  
  

• Keyword: neural networks; solar energy; power generation; prediction algorithms;
• DOI: 10.12250/jpciams2023090805
• Citation form: Yang Liu.Solar power prediction algorithm based on improved BP neural network [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.21-25

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