Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model

Xi Fang; An Yuan; Yao Jiang; Wei Qian; Wang Yuyao

doi:doi:10.11648/j.sd.20160406.11

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Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model

Xi Fang, An Yuan, Yao Jiang, Wei Qian, Wang Yuyao

Published in Science Discovery (Volume 4, Issue 6)

Received: 22 November 2016 Published: 24 November 2016

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Abstract

The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.

Published in	Science Discovery (Volume 4, Issue 6)
DOI	10.11648/j.sd.20160406.11
Page(s)	353-359
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

PV Power Station, Output Prediction, BP Neural Network Model, ARMA Model, Nonlinear Combination Model

References

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APA Style

Xi Fang, An Yuan, Yao Jiang, Wei Qian, Wang Yuyao. (2016). Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model. Science Discovery, 4(6), 353-359. https://doi.org/10.11648/j.sd.20160406.11

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ACS Style

Xi Fang; An Yuan; Yao Jiang; Wei Qian; Wang Yuyao. Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model. Sci. Discov. 2016, 4(6), 353-359. doi: 10.11648/j.sd.20160406.11

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AMA Style

Xi Fang, An Yuan, Yao Jiang, Wei Qian, Wang Yuyao. Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model. Sci Discov. 2016;4(6):353-359. doi: 10.11648/j.sd.20160406.11

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@article{10.11648/j.sd.20160406.11,
  author = {Xi Fang and An Yuan and Yao Jiang and Wei Qian and Wang Yuyao},
  title = {Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model},
  journal = {Science Discovery},
  volume = {4},
  number = {6},
  pages = {353-359},
  doi = {10.11648/j.sd.20160406.11},
  url = {https://doi.org/10.11648/j.sd.20160406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160406.11},
  abstract = {The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.},
 year = {2016}
}

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TY  - JOUR
T1  - Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model
AU  - Xi Fang
AU  - An Yuan
AU  - Yao Jiang
AU  - Wei Qian
AU  - Wang Yuyao
Y1  - 2016/11/24
PY  - 2016
N1  - https://doi.org/10.11648/j.sd.20160406.11
DO  - 10.11648/j.sd.20160406.11
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 353
EP  - 359
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20160406.11
AB  - The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.
VL  - 4
IS  - 6
ER  -

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Author Information

Xi Fang

Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, xi’an, China
An Yuan

Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, xi’an, China
Yao Jiang

Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, xi’an, China
Wei Qian

Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, xi’an, China
Wang Yuyao

Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, xi’an, China

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Plain Text BibTeX RIS

APA Style

Xi Fang, An Yuan, Yao Jiang, Wei Qian, Wang Yuyao. (2016). Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model. Science Discovery, 4(6), 353-359. https://doi.org/10.11648/j.sd.20160406.11

Copy | Download

ACS Style

Xi Fang; An Yuan; Yao Jiang; Wei Qian; Wang Yuyao. Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model. Sci. Discov. 2016, 4(6), 353-359. doi: 10.11648/j.sd.20160406.11

Copy | Download

AMA Style

Xi Fang, An Yuan, Yao Jiang, Wei Qian, Wang Yuyao. Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model. Sci Discov. 2016;4(6):353-359. doi: 10.11648/j.sd.20160406.11

Copy | Download

@article{10.11648/j.sd.20160406.11,
  author = {Xi Fang and An Yuan and Yao Jiang and Wei Qian and Wang Yuyao},
  title = {Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model},
  journal = {Science Discovery},
  volume = {4},
  number = {6},
  pages = {353-359},
  doi = {10.11648/j.sd.20160406.11},
  url = {https://doi.org/10.11648/j.sd.20160406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160406.11},
  abstract = {The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model
AU  - Xi Fang
AU  - An Yuan
AU  - Yao Jiang
AU  - Wei Qian
AU  - Wang Yuyao
Y1  - 2016/11/24
PY  - 2016
N1  - https://doi.org/10.11648/j.sd.20160406.11
DO  - 10.11648/j.sd.20160406.11
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 353
EP  - 359
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20160406.11
AB  - The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.
VL  - 4
IS  - 6
ER  -

Copy | Download