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Short Term Prediction of Photovoltaic Generation Output Based on Similar Days

Received: 29 November 2016     Published: 1 December 2016
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Abstract

Based on the analysis of the main weather factors that affect the output of the PV generation, a feedback based neural network prediction model based on similar days is proposed. From the historical data of the weather, the weather similar days are selected, the Elman neural network prediction model is established to predict the output power of the photovoltaic power generation combined with the similar days of the power generation and the similar days and the weather data.

Published in Science Discovery (Volume 4, Issue 6)
DOI 10.11648/j.sd.20160406.16
Page(s) 380-386
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Photovoltaic Power Generation, Power Prediction, Neural Network

References
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[2] Rikos E, Tselepis E, Hoyer Klick C, et a1. Stability and power quality issues in microgrids under weather disturbances study of photovoltaic integration [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2008, 1 (3): 170-180.
[3] Paatero J V, Lund P D.Effects of large-scale photovoltaic power integration on electricity distribution networks [J].Renewable Energy, 2007, 32 (10): 216-234.
[4] Alquthami T, Ravindra H, Farnque M, et a1. Study of photovoltaic integration impact on system stability using custom model of PV arrays integrated with PSS//E[C]//2010 North American Power Symposium, Arlington, TX, USA: IEEE, 2010: 1-8.
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[6] 代倩,段善旭,蔡涛,等.基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J].中国电机工程学报,2011,31(34):28-35。
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[9] 代倩,段善旭,蔡涛,等.基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J].中国电机工程学报,2011,31(34):28-35。
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  • APA Style

    Cui Hanjun, Yao Lixiao. (2016). Short Term Prediction of Photovoltaic Generation Output Based on Similar Days. Science Discovery, 4(6), 380-386. https://doi.org/10.11648/j.sd.20160406.16

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    ACS Style

    Cui Hanjun; Yao Lixiao. Short Term Prediction of Photovoltaic Generation Output Based on Similar Days. Sci. Discov. 2016, 4(6), 380-386. doi: 10.11648/j.sd.20160406.16

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    AMA Style

    Cui Hanjun, Yao Lixiao. Short Term Prediction of Photovoltaic Generation Output Based on Similar Days. Sci Discov. 2016;4(6):380-386. doi: 10.11648/j.sd.20160406.16

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  • @article{10.11648/j.sd.20160406.16,
      author = {Cui Hanjun and Yao Lixiao},
      title = {Short Term Prediction of Photovoltaic Generation Output Based on Similar Days},
      journal = {Science Discovery},
      volume = {4},
      number = {6},
      pages = {380-386},
      doi = {10.11648/j.sd.20160406.16},
      url = {https://doi.org/10.11648/j.sd.20160406.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160406.16},
      abstract = {Based on the analysis of the main weather factors that affect the output of the PV generation, a feedback based neural network prediction model based on similar days is proposed. From the historical data of the weather, the weather similar days are selected, the Elman neural network prediction model is established to predict the output power of the photovoltaic power generation combined with the similar days of the power generation and the similar days and the weather data.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - Short Term Prediction of Photovoltaic Generation Output Based on Similar Days
    AU  - Cui Hanjun
    AU  - Yao Lixiao
    Y1  - 2016/12/01
    PY  - 2016
    N1  - https://doi.org/10.11648/j.sd.20160406.16
    DO  - 10.11648/j.sd.20160406.16
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 380
    EP  - 386
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20160406.16
    AB  - Based on the analysis of the main weather factors that affect the output of the PV generation, a feedback based neural network prediction model based on similar days is proposed. From the historical data of the weather, the weather similar days are selected, the Elman neural network prediction model is established to predict the output power of the photovoltaic power generation combined with the similar days of the power generation and the similar days and the weather data.
    VL  - 4
    IS  - 6
    ER  - 

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Author Information
  • Institute of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an, China

  • Institute of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an, China

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