Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network

Samuel Bimenyimana; Godwin Norense Osarumwense Asemota; Li Lingling

doi:doi:10.11648/j.jeece.20170202.13

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Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network

Samuel Bimenyimana, Godwin Norense Osarumwense Asemota, Li Lingling

Published in Journal of Energy, Environmental & Chemical Engineering (Volume 2, Issue 2)

Received: 1 June 2017 Accepted: 15 June 2017 Published: 26 July 2017

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Abstract

Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.

Published in	Journal of Energy, Environmental & Chemical Engineering (Volume 2, Issue 2)
DOI	10.11648/j.jeece.20170202.13
Page(s)	32-40
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), 2017. Published by Science Publishing Group

Keywords

Photovoltaic Output Power, Prediction, Empirical Formula, Temperature, Nonlinear Autoregressive Neural Network

References

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APA Style

Samuel Bimenyimana, Godwin Norense Osarumwense Asemota, Li Lingling. (2017). Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network. Journal of Energy, Environmental & Chemical Engineering, 2(2), 32-40. https://doi.org/10.11648/j.jeece.20170202.13

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ACS Style

Samuel Bimenyimana; Godwin Norense Osarumwense Asemota; Li Lingling. Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network. J. Energy Environ. Chem. Eng. 2017, 2(2), 32-40. doi: 10.11648/j.jeece.20170202.13

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AMA Style

Samuel Bimenyimana, Godwin Norense Osarumwense Asemota, Li Lingling. Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network. J Energy Environ Chem Eng. 2017;2(2):32-40. doi: 10.11648/j.jeece.20170202.13

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@article{10.11648/j.jeece.20170202.13,
  author = {Samuel Bimenyimana and Godwin Norense Osarumwense Asemota and Li Lingling},
  title = {Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network},
  journal = {Journal of Energy, Environmental & Chemical Engineering},
  volume = {2},
  number = {2},
  pages = {32-40},
  doi = {10.11648/j.jeece.20170202.13},
  url = {https://doi.org/10.11648/j.jeece.20170202.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeece.20170202.13},
  abstract = {Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.},
 year = {2017}
}

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TY  - JOUR
T1  - Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network
AU  - Samuel Bimenyimana
AU  - Godwin Norense Osarumwense Asemota
AU  - Li Lingling
Y1  - 2017/07/26
PY  - 2017
N1  - https://doi.org/10.11648/j.jeece.20170202.13
DO  - 10.11648/j.jeece.20170202.13
T2  - Journal of Energy, Environmental & Chemical Engineering
JF  - Journal of Energy, Environmental & Chemical Engineering
JO  - Journal of Energy, Environmental & Chemical Engineering
SP  - 32
EP  - 40
PB  - Science Publishing Group
SN  - 2637-434X
UR  - https://doi.org/10.11648/j.jeece.20170202.13
AB  - Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.
VL  - 2
IS  - 2
ER  -

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Author Information

Samuel Bimenyimana

Department of Electrical Engineering, Hebei University of Technology, Tianjin, China
Godwin Norense Osarumwense Asemota

Department of Electrical Engineering, University of Rwanda, School of Engineering Kigali, Rwanda
Li Lingling

Department of Electrical Engineering, Hebei University of Technology, Tianjin, China

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APA Style

Samuel Bimenyimana, Godwin Norense Osarumwense Asemota, Li Lingling. (2017). Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network. Journal of Energy, Environmental & Chemical Engineering, 2(2), 32-40. https://doi.org/10.11648/j.jeece.20170202.13

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ACS Style

Samuel Bimenyimana; Godwin Norense Osarumwense Asemota; Li Lingling. Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network. J. Energy Environ. Chem. Eng. 2017, 2(2), 32-40. doi: 10.11648/j.jeece.20170202.13

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AMA Style

Samuel Bimenyimana, Godwin Norense Osarumwense Asemota, Li Lingling. Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network. J Energy Environ Chem Eng. 2017;2(2):32-40. doi: 10.11648/j.jeece.20170202.13

Copy | Download

@article{10.11648/j.jeece.20170202.13,
  author = {Samuel Bimenyimana and Godwin Norense Osarumwense Asemota and Li Lingling},
  title = {Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network},
  journal = {Journal of Energy, Environmental & Chemical Engineering},
  volume = {2},
  number = {2},
  pages = {32-40},
  doi = {10.11648/j.jeece.20170202.13},
  url = {https://doi.org/10.11648/j.jeece.20170202.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeece.20170202.13},
  abstract = {Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network
AU  - Samuel Bimenyimana
AU  - Godwin Norense Osarumwense Asemota
AU  - Li Lingling
Y1  - 2017/07/26
PY  - 2017
N1  - https://doi.org/10.11648/j.jeece.20170202.13
DO  - 10.11648/j.jeece.20170202.13
T2  - Journal of Energy, Environmental & Chemical Engineering
JF  - Journal of Energy, Environmental & Chemical Engineering
JO  - Journal of Energy, Environmental & Chemical Engineering
SP  - 32
EP  - 40
PB  - Science Publishing Group
SN  - 2637-434X
UR  - https://doi.org/10.11648/j.jeece.20170202.13
AB  - Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.
VL  - 2
IS  - 2
ER  -

Copy | Download