Short Term Prediction of Photovoltaic Generation Output Based on Similar Days

Cui Hanjun; Yao Lixiao

doi:doi:10.11648/j.sd.20160406.16

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Short Term Prediction of Photovoltaic Generation Output Based on Similar Days

Cui Hanjun, Yao Lixiao

Published in Science Discovery (Volume 4, Issue 6)

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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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

Cui Hanjun

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

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

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download