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Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation

Author

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  • Chi Hua
  • Erxi Zhu
  • Liang Kuang
  • Dechang Pi

Abstract

Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

Suggested Citation

  • Chi Hua & Erxi Zhu & Liang Kuang & Dechang Pi, 2019. "Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:10:p:1550147719883134
    DOI: 10.1177/1550147719883134
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    References listed on IDEAS

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    Cited by:

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    2. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
    3. Yan Guo & Dezhao Tang & Wei Tang & Senqi Yang & Qichao Tang & Yang Feng & Fang Zhang, 2022. "Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors," Sustainability, MDPI, vol. 14(17), pages 1-18, August.

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