Journal of Hebei University(Natural Science Edition) >

2021 , Vol. 41 >Issue 3: 238 - 244

DOI: https://doi.org/10.3969/j.issn.1000-1565.2021.03.003

Photovoltaic power generation prediction of rail transit based on improved support vector machine

  • HUANG Yuansheng ,
  • TIAN Lixia ,
  • SUN Shize ,
  • DENG Jiajia ,
  • ZHAO Hengfeng
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  • 1.College of Economy and Management, North China Electric Power University, Baoding 071003, China; 2.State Grid Communication Canpany, Beijing 100052, China; 3.PetroChina North China Petrochemical Company, Renqiu 062552, China

Received date: 2021-01-13

  Online published: 2021-05-28

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Abstract

Rail transit system is a big power consumer. Connecting photovoltaic power generation system to rail transit system can not only reduce the operation cost of transportation system, but also realize energy saving and environmental protection. However, due to the randomness and uncertainty of photovoltaic power generation, the direct access of photovoltaic power generation to rail transit power supply system will bring a certain impact on rail transit power supply system. Accurate photovoltaic power generation prediction is an efective way to reduce the impact of photovoltaic grid connection. Firstly, the adaptive particle swarm optimization algorithm is used to improve the optimization ability of the particles in the training of the historical data of photovoltaic power generation, and then the optimized parameters are substituted into the least squares support vector machine to predict the photovoltaic power generation load, which effectively ensures the accuracy of the prediction, and thus improves the stability of the photovoltaic power generation when connected to the rail transit power supply system.

Key words： rail transit; PV generation; support vector machine(SVM); forecasting

Cite this article

HUANG Yuansheng , TIAN Lixia , SUN Shize , DENG Jiajia , ZHAO Hengfeng . Photovoltaic power generation prediction of rail transit based on improved support vector machine[J]. Journal of Hebei University(Natural Science Edition), 2021 , 41(3) : 238 -244 . DOI: 10.3969/j.issn.1000-1565.2021.03.003

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