Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

Ulrich Thierry Prodjinotho; Patrice Koffi Chetangny; Macaire Bienvenu Agbomahena; Victor Zogbochi; Laurent Medewou; Gerald Barbier; Didier Chamagne

doi:doi:10.11648/j.ijepe.20241302.11

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| Peer-Reviewed

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

Ulrich Thierry Prodjinotho^*

, Patrice Koffi Chetangny

, Macaire Bienvenu Agbomahena

, Victor Zogbochi

, Laurent Medewou

, Gerald Barbier, Didier Chamagne

Published in International Journal of Energy and Power Engineering (Volume 13, Issue 2)

Received: 27 February 2024 Accepted: 14 March 2024 Published: 2 April 2024

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Abstract

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

Published in	International Journal of Energy and Power Engineering (Volume 13, Issue 2)
DOI	10.11648/j.ijepe.20241302.11
Page(s)	21-31
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), 2024. Published by Science Publishing Group

Keywords

Power Demand, Energy Supply, Maximum Power Peak, Forecasting, Energy Planning, Interconnected Network

References

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Prodjinotho, U. T., Chetangny, P. K., Agbomahena, M. B., Zogbochi, V., Medewou, L., et al. (2024). Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032 . International Journal of Energy and Power Engineering, 13(2), 21-31. https://doi.org/10.11648/j.ijepe.20241302.11

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ACS Style

Prodjinotho, U. T.; Chetangny, P. K.; Agbomahena, M. B.; Zogbochi, V.; Medewou, L., et al. Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032 . Int. J. Energy Power Eng. 2024, 13(2), 21-31. doi: 10.11648/j.ijepe.20241302.11

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AMA Style

Prodjinotho UT, Chetangny PK, Agbomahena MB, Zogbochi V, Medewou L, et al. Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032 . Int J Energy Power Eng. 2024;13(2):21-31. doi: 10.11648/j.ijepe.20241302.11

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@article{10.11648/j.ijepe.20241302.11,
  author = {Ulrich Thierry Prodjinotho and Patrice Koffi Chetangny and Macaire Bienvenu Agbomahena and Victor Zogbochi and Laurent Medewou and Gerald Barbier and Didier Chamagne},
  title = {Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032
},
  journal = {International Journal of Energy and Power Engineering},
  volume = {13},
  number = {2},
  pages = {21-31},
  doi = {10.11648/j.ijepe.20241302.11},
  url = {https://doi.org/10.11648/j.ijepe.20241302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20241302.11},
  abstract = {The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.
},
 year = {2024}
}

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TY  - JOUR
T1  - Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

AU  - Ulrich Thierry Prodjinotho
AU  - Patrice Koffi Chetangny
AU  - Macaire Bienvenu Agbomahena
AU  - Victor Zogbochi
AU  - Laurent Medewou
AU  - Gerald Barbier
AU  - Didier Chamagne
Y1  - 2024/04/02
PY  - 2024
N1  - https://doi.org/10.11648/j.ijepe.20241302.11
DO  - 10.11648/j.ijepe.20241302.11
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 21
EP  - 31
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20241302.11
AB  - The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

VL  - 13
IS  - 2
ER  -

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Author Information

Ulrich Thierry Prodjinotho

Laboratory of Electrotechnics, Telecommunications and Applied Computing, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email

http://orcid.org/0009-0000-4920-2074
Patrice Koffi Chetangny

Laboratory of Electrotechnics, Telecommunications and Applied Computing, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email

http://orcid.org/0009-0001-4540-8427
Macaire Bienvenu Agbomahena

Laboratory of Electrotechnics, Telecommunications and Applied Computing, University of Abomey-Calavi, Abomey-Calavi, Benin

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http://orcid.org/0000-0002-0305-5352
Victor Zogbochi

Laboratory of Electrotechnics, Telecommunications and Applied Computing, University of Abomey-Calavi, Abomey-Calavi, Benin

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http://orcid.org/0009-0007-0324-5985
Laurent Medewou

Laboratory of Electrotechnics, Telecommunications and Applied Computing, University of Abomey-Calavi, Abomey-Calavi, Benin

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http://orcid.org/0009-0003-6321-2844
Gerald Barbier

Laboratory of Textile Physics and Mechanics (LPMT), University of Haute Alsace Mulhouse, Haute Alsace Mulhouse, France

Contact Email
Didier Chamagne

Energy Department, FEMTO-ST, University of Franche-Comte, Belfort, France

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APA Style

Prodjinotho, U. T., Chetangny, P. K., Agbomahena, M. B., Zogbochi, V., Medewou, L., et al. (2024). Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032 . International Journal of Energy and Power Engineering, 13(2), 21-31. https://doi.org/10.11648/j.ijepe.20241302.11

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ACS Style

Prodjinotho, U. T.; Chetangny, P. K.; Agbomahena, M. B.; Zogbochi, V.; Medewou, L., et al. Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032 . Int. J. Energy Power Eng. 2024, 13(2), 21-31. doi: 10.11648/j.ijepe.20241302.11

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AMA Style

Prodjinotho UT, Chetangny PK, Agbomahena MB, Zogbochi V, Medewou L, et al. Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032 . Int J Energy Power Eng. 2024;13(2):21-31. doi: 10.11648/j.ijepe.20241302.11

Copy | Download

@article{10.11648/j.ijepe.20241302.11,
  author = {Ulrich Thierry Prodjinotho and Patrice Koffi Chetangny and Macaire Bienvenu Agbomahena and Victor Zogbochi and Laurent Medewou and Gerald Barbier and Didier Chamagne},
  title = {Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032
},
  journal = {International Journal of Energy and Power Engineering},
  volume = {13},
  number = {2},
  pages = {21-31},
  doi = {10.11648/j.ijepe.20241302.11},
  url = {https://doi.org/10.11648/j.ijepe.20241302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20241302.11},
  abstract = {The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

AU  - Ulrich Thierry Prodjinotho
AU  - Patrice Koffi Chetangny
AU  - Macaire Bienvenu Agbomahena
AU  - Victor Zogbochi
AU  - Laurent Medewou
AU  - Gerald Barbier
AU  - Didier Chamagne
Y1  - 2024/04/02
PY  - 2024
N1  - https://doi.org/10.11648/j.ijepe.20241302.11
DO  - 10.11648/j.ijepe.20241302.11
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 21
EP  - 31
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20241302.11
AB  - The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

VL  - 13
IS  - 2
ER  -

Copy | Download