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Prediction of energy photovoltaic power generation based on artificial intelligence algorithm

  • S.I. : ATCI 2020
  • Published:
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Abstract

The key to the coordination of photovoltaic power generation and conventional energy power load lies in the accurate prediction of photovoltaic power generation. At present, prediction models have problems with accuracy and system operation stability. Based on the neural network algorithm, this research carries the prediction of energy photovoltaic power generation and establishes a BP neural network prediction model and a wavelet neural network prediction model. Moreover, this research studies the influence of various factors on the prediction t of photovoltaic power generation, and analyzes the relationship between the various factors. In addition, in this study, a comparative test is constructed to analyze the model performance, and a statistical graph is drawn to take a visual comparison of performance. The research shows that the model proposed in this paper has certain effects and has certain advantages in the prediction of photovoltaic power generation.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, 102206, China

    Shuhua Zhang

  2. China Electric Power Research Institute Co., Ltd., Beijing, 100192, China

    Shuhua Zhang & Jie Tong

  3. Department of Electrical, Computer, and System Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Jinsong Wang

  4. Department of Science and Technology, State Grid Inner Mongolia Eastern Power Co., Ltd, Hohho, 010010, China

    Haibo Liu

  5. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, 071003, China

    Zheng Sun

Authors
  1. Shuhua Zhang
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  2. Jinsong Wang
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  3. Haibo Liu
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  4. Jie Tong
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  5. Zheng Sun
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Correspondence to Shuhua Zhang.

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Zhang, S., Wang, J., Liu, H. et al. Prediction of energy photovoltaic power generation based on artificial intelligence algorithm. Neural Comput & Applic 33, 821–835 (2021). https://doi.org/10.1007/s00521-020-05249-z

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  • DOI: https://doi.org/10.1007/s00521-020-05249-z

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