Research Article | | Peer-Reviewed

Predicting Ghana’s Daily Natural Gas Consumption Using Time Series Models

Received: 13 January 2024     Accepted: 25 January 2024     Published: 7 March 2024
Views:       Downloads:
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.

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

Keywords

ARIMA, SARIMA, Natural Gas Consumption, Mean Absolute Percentage Error, Root Mean Square Error, Time Series Analysis

References
[1] Boran FE (2015) Forecasting natural gas consumption in Turkey using grey prediction. Energy Sources, Part B: Economics, Planning, and Policy 10(2): 208-213. https://doi.org/10.1080/15567249.2014.893040
[2] Broni-Bediako E, Dankwa OK (2013) Assessment of liquefied petroleum gas utilisation in Ghana – a study at Tarkwa. International Journal of Scientific and Technology Research 2(9): pp. 6-10.
[3] Broni-Bediako E, Amorin R (2018) The Ghana liquefied petroleum gas promotion programme: opportunities, challenges and the way Forward. Innov Ener Res 7(2): 1-5.
[4] Kumi EN (2017) The electricity situation in Ghana: challenges and opportunities. CGD Policy Paper.
[5] Ayaburi J, Bazilian M (2020) Economic benefits of natural gas production: The case of Ghana’s Sankofa Gas Project. Energy for Growth Hub1-2.
[6] Anon (2023) Natural gas consumption. https://www.ghanagas.com.gh/en/page/natural-gas-consumption-58
[7] Adom PK, Bekoe W, Akoena SKK (2012) Modelling aggregate domestic electricity demand in Ghana: An autoregressive distributed lag bounds co-integration approach. Energy Policy 42: 530-537. https://doi.org/10.1016/j.enpol.2011.12.019
[8] Shin S, Cho M (2022) Green supply chain management implemented by suppliers as drivers for SMEs environmental growth with a focus on the restaurant industry. Sustainability 14(6): pp. 3515. https://doi.org/10.3390/su14063515
[9] Gao F, Shao X (2021) Forecasting annual natural gas consumption via the application of a novel hybrid model. Environ Sci Pollut Res 21411–21424. https://doi.org/10.1007/s11356-020-12275-w
[10] Owusu PA, Asumadu-Sarkodie S (2016) A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Engineering 3(1). https://doi.org/10.1080/23311916.2016.1167990
[11] Kemausuor F, Obeng GY, Brew-Hammond A, et al. (2011) A review of trends, policies and plans for increasing energy access in Ghana. Renewable and Sustainable Energy Reviews 15(9): 5143-5154. https://doi.org/10.1016/j.rser.2011.07.041
[12] Scarpa F, Bianco V (2017) Assessing the quality of natural gas consumption forecasting: an application to the Italian residential sector. Energies 10: 13. https://doi.org/10.3390/en10111879
[13] Cardoso CV, Cruz GL (2016) Forecasting natural gas consumption using ARIMA models and artificial neural networks. IEEE Latin America Transactions 14: 2233-2238. https://doi.org/10.1109/TLA.2016.7530418
[14] Chkili W, Hammoudeh S, Nguyen DK (2014) Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Energy Economics 41: 1-18. https://doi.org/10.1016/j.eneco.2013.10.011
[15] Feng X, Zhang J, Zou S, et al. (2014) Study on natural gas demand prediction model in China. Advanced Materials Research 962-965.
[16] Vondracek J, Pelikan E, Konar O, et al. (2008) A statistical model for the estimation of natural gas consumption. Applied Energy 85(5): 362-370.
[17] Khan MA (2015) Modeling and forecasting the demand for natural gas. Renewable and Sustainable Energy Reviews 49: 1145-1159.
[18] Bianco V, Scarpa F, Tagliafico L (2014) Scenario analysis of nonresidential natural gas consumption in Italy. Applied Energy 113: 392-403. https://doi.org/10.1016/j.apenergy.2013.07.054
[19] Kani AH, Abasspour M, Abedi Z (2014) Estimation of demand function for natural gas in Iran: Evidences based on smooth transition regression models. Economic Modeling 36: 341-347.
[20] Beyca OF, Ervural BC, Tatoglu E, et al. (2019) Using machine learning tools for forecasting natural gas consumption in the province of Istanbul. Energy Economics 80: 937-949. https://doi.org/10.1016/j.eneco.2019.03.006
[21] Behrouznia A, Saberi M, Azadeh A, et al. (2010) An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and Analysis: The case of South America. International Conference on Intelligent and Advanced System 5716160. https://doi.org/10.1109/ICIAS.2010.5716160
[22] Viet NH, Mandziuk J (2005) Neural and fuzzy neural networks in prediction of natural gas consumption. Neural Parallel Scientific Computation 13(3-4): 265-286. https://doi.org/10.1109/NNSP.2003.1318075
[23] Liu T (2017) Statistical inference of partially linear panel data regression models with fixed individual and time effects. Communications in Statistics - Theory and Methods 46(15): 7267-7288. https://doi.org/10.1080/03610926.2015.1116577
[24] Godfrey LG, Tremayne AR (1988). Checks of model adequacy for univariate time series models and their application to econometric relationships. Econometric Reviews 7(1): 1-42. https://doi.org/10.1080/07474938808800138
[25] Carbó JM, Gorjon S (2022) Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin. SSRN Electronic Journal 4: 24-56. https://doi.org/10.2139/ssrn.4087481
[26] EdigerVŞ, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35(3): 1701-1708. https://doi.org/10.1016/j.enpol.2006.05.009
[27] Valipour M (2015) Long-term Runoff study using SARIMA and ARIMA models in the United States. Meteorological Applications 22(3): 592-598. https://doi.org/10.1002/met.1491
[28] Akpinar M, Yumusak N (2016) Year ahead demand forecast of city natural gas using seasonal time series methods. Energies 9(9): 1-17. https://doi.org/10.3390/en9090727
[29] Erdogdu E (2010) Natural gas demand in Turkey. Applied Energy 87(1): 211-219.
[30] Akkurt M, Demirel O, Zain S (2010) Forecasting Turkey’s natural gas consumption by using time series methods. European Journal of Economic and Political Studies 3(2): 1-21.
[31] Munandar D (2019) Multilayer Perceptron (MLP) and Autoregressive Integrated Moving Average (ARIMA) models in multivariate input time series data: solar irradiance forecasting. International Journal on Advanced Science, Engineering and Information Technology 9(1): 220.
[32] Fossati S (2013) Unit root testing with stationary covariates and a structural break in the trend function. Journal of Time Series Analysis 34(3): 368-384. https://doi.org/10.1111/jtsa.12020
[33] Dickey D, Fuller W (1979) Divolatility of the estimators for autoregressive time series with a unit root”. Journal of American Statistical Association 74(366): 427-431.
[34] Kwiatkowski D, Phillips PCB, Schmidt P., et al. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54(3): 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
[35] Kofi A, Albert B, Joseph A (2019) Modelling the volatility of the price of bitcoin. American Journal of Mathematics and Statistics 9(4): 151-159.
[36] Hyndman RJ, Athanasopoulos G (2018) Forecasting: Principles and practice”, OTexts Publications, London.
[37] Ismail MA, El-Metaal EMA (2020) Forecasting residential natural gas consumption in Egypt. Journal of Humanities and Applied Social Sciences 2(4): 297-308. https://doi.org/10.1108/JHASS-03-2020-0046
[38] Zhang X, Pang Y, Cui M, et al. (2015) Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann. Epidemiol. 25: 101-106. https://doi.org/10.1016/j.annepidem.2014.10.015
[39] Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int. J. Forecast 22: 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
[40] Armstrong JS, Collopy F (1992) Error measures for generalizing about forecasting methods: Empirical comparisons. Int. J. Forecast 8: 69-80. https://doi.org/10.1016/0169-2070(92)90008-W
Cite This Article
  • 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

    Copy | Download

    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

    Copy | Download

    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

    Copy | 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}
    }
    

    Copy | Download

  • 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  - 

    Copy | Download

Author Information
  • Department of Petroleum and Natural Gas Engineering, School of Petroleum Studies, University of Mines and Technology, Tarkwa, Ghana

  • Department of Mathematical Sciences, Faculty of Computing and Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana

  • Department of Petroleum and Natural Gas Engineering, School of Petroleum Studies, University of Mines and Technology, Tarkwa, Ghana

  • Sections