In recent years, natural gas utilisation has seen a considerable increase because, it presents an alternative energy source that is reliable, economical and environmentally friendly for consumers. In Ghana, natural gas consumption has over the years increased due to mainly the rise in industrial and residential demands. Accurate prediction of natural gas consumption will provide stakeholders with vital information needed for planning and making informed policy decisions. This paper explores the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict Ghana's daily natural gas consumption. The data employed for the study is daily natural gas consumption in Ghana from 2020 to 2022. The results show that both ARIMA and SARIMA models can predict the consumption of natural gas in Ghana with a good degree of accuracy. The SARIMA model slightly outperforms the ARIMA model, with a Root Mean Square Error (RMSE) of 22.25 and a Mean Absolute Percentage Error (MAPE) of 6.96%, compared to an RMSE of 23.27 and a MAPE of 7.29% for the ARIMA model. The model forecast suggests a steady natural gas consumption in Ghana but with some intermittent fluctuations.
| Published in | Petroleum Science and Engineering (Volume 8, Issue 1) |
| DOI | 10.11648/j.pse.20240801.14 |
| Page(s) | 27-37 |
| 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 |
ARIMA, SARIMA, Natural Gas Consumption, Mean Absolute Percentage Error, Root Mean Square Error, Time Series Analysis
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APA Style
Broni-Bediako, E., Buabeng, A., Allotey, P. (2024). Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models. Petroleum Science and Engineering, 8(1), 27-37. https://doi.org/10.11648/j.pse.20240801.14
ACS Style
Broni-Bediako, E.; Buabeng, A.; Allotey, P. Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models. Pet. Sci. Eng. 2024, 8(1), 27-37. doi: 10.11648/j.pse.20240801.14
AMA Style
Broni-Bediako E, Buabeng A, Allotey P. Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models. Pet Sci Eng. 2024;8(1):27-37. doi: 10.11648/j.pse.20240801.14
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Download@article{10.11648/j.pse.20240801.14,
author = {Eric Broni-Bediako and Albert Buabeng and Philip Allotey},
title = {Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models},
journal = {Petroleum Science and Engineering},
volume = {8},
number = {1},
pages = {27-37},
doi = {10.11648/j.pse.20240801.14},
url = {https://doi.org/10.11648/j.pse.20240801.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20240801.14},
abstract = {In recent years, natural gas utilisation has seen a considerable increase because, it presents an alternative energy source that is reliable, economical and environmentally friendly for consumers. In Ghana, natural gas consumption has over the years increased due to mainly the rise in industrial and residential demands. Accurate prediction of natural gas consumption will provide stakeholders with vital information needed for planning and making informed policy decisions. This paper explores the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict Ghana's daily natural gas consumption. The data employed for the study is daily natural gas consumption in Ghana from 2020 to 2022. The results show that both ARIMA and SARIMA models can predict the consumption of natural gas in Ghana with a good degree of accuracy. The SARIMA model slightly outperforms the ARIMA model, with a Root Mean Square Error (RMSE) of 22.25 and a Mean Absolute Percentage Error (MAPE) of 6.96%, compared to an RMSE of 23.27 and a MAPE of 7.29% for the ARIMA model. The model forecast suggests a steady natural gas consumption in Ghana but with some intermittent fluctuations.
},
year = {2024}
}
TY - JOUR T1 - Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models AU - Eric Broni-Bediako AU - Albert Buabeng AU - Philip Allotey Y1 - 2024/03/07 PY - 2024 N1 - https://doi.org/10.11648/j.pse.20240801.14 DO - 10.11648/j.pse.20240801.14 T2 - Petroleum Science and Engineering JF - Petroleum Science and Engineering JO - Petroleum Science and Engineering SP - 27 EP - 37 PB - Science Publishing Group SN - 2640-4516 UR - https://doi.org/10.11648/j.pse.20240801.14 AB - In recent years, natural gas utilisation has seen a considerable increase because, it presents an alternative energy source that is reliable, economical and environmentally friendly for consumers. In Ghana, natural gas consumption has over the years increased due to mainly the rise in industrial and residential demands. Accurate prediction of natural gas consumption will provide stakeholders with vital information needed for planning and making informed policy decisions. This paper explores the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict Ghana's daily natural gas consumption. The data employed for the study is daily natural gas consumption in Ghana from 2020 to 2022. The results show that both ARIMA and SARIMA models can predict the consumption of natural gas in Ghana with a good degree of accuracy. The SARIMA model slightly outperforms the ARIMA model, with a Root Mean Square Error (RMSE) of 22.25 and a Mean Absolute Percentage Error (MAPE) of 6.96%, compared to an RMSE of 23.27 and a MAPE of 7.29% for the ARIMA model. The model forecast suggests a steady natural gas consumption in Ghana but with some intermittent fluctuations. VL - 8 IS - 1 ER -
Department of Petroleum and Natural Gas Engineering, School of Petroleum Studies, University of Mines and Technology, Tarkwa, Ghana
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