IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i4p1586-d1591431.html
   My bibliography  Save this article

Enhancing Energy Consumption in Automotive Component Manufacturing: A Hybrid Autoregressive Integrated Moving Average–Long Short-Term Memory Prediction Model

Author

Listed:
  • Ragosebo Kgaugelo Modise

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Khumbulani Mpofu

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Tshifhiwa Nenzhelele

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Olukorede Tijani Adenuga

    (Department of Industrial Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

The automotive industry faces continuing challenges with regard to advancing sustainability and reducing energy consumption and vehicle emissions. South Africa accounts for half of the total CO 2 emissions in Africa and is the world’s 12th-largest CO 2 emitter. In this study, we aimed to develop a model combining autoregressive integrated moving averages (ARIMAs) with long short-term memory (LSTM) to determine the best fit for prediction using the lowest root mean square error configuration and enhance energy consumption in automotive component manufacturing. The ARIMA model dissects time-series data into the components of level, trend, and seasonality, while the automatic ARIMA function refines the model parameters. Simultaneously, utilizing historical data, the LSTM model uses specific algorithms to predict future electricity generation and carbon emissions for the automotive component’s manufacturing sector. According to our results, the predicted variables’ interdependence revealed an enhancement in energy intensity for vehicle body part products equal to 29%, a cumulative energy savings of 7.22%, and an increase in energy efficiency equal to 16.25%. Our model’s predictive fitness holds significant potential for allowing automotive component manufacturers to make informed economic and technical decisions toward the development of low-carbon products. Critically, improved energy efficiency in automotive component manufacturing activities is critical for lowering energy consumption, greenhouse gas emissions, sustainable transportation, and production costs.

Suggested Citation

  • Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Tshifhiwa Nenzhelele & Olukorede Tijani Adenuga, 2025. "Enhancing Energy Consumption in Automotive Component Manufacturing: A Hybrid Autoregressive Integrated Moving Average–Long Short-Term Memory Prediction Model," Sustainability, MDPI, vol. 17(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1586-:d:1591431
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/4/1586/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/4/1586/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernando Enzo Kenta Sato & Toshihiko Nakata, 2020. "Energy Consumption Analysis for Vehicle Production through a Material Flow Approach," Energies, MDPI, vol. 13(9), pages 1-18, May.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Elżbieta Macioszek & Anna Granà & Paulo Fernandes & Margarida C. Coelho, 2022. "New Perspectives and Challenges in Traffic and Transportation Engineering Supporting Energy Saving in Smart Cities—A Multidisciplinary Approach to a Global Problem," Energies, MDPI, vol. 15(12), pages 1-8, June.
    4. Samour, Ahmed & Moyo, Delani & Tursoy, Turgut, 2022. "Renewable energy, banking sector development, and carbon dioxide emissions nexus: A path toward sustainable development in South Africa," Renewable Energy, Elsevier, vol. 193(C), pages 1032-1040.
    5. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    6. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    7. Giampieri, A. & Ling-Chin, J. & Ma, Z. & Smallbone, A. & Roskilly, A.P., 2020. "A review of the current automotive manufacturing practice from an energy perspective," Applied Energy, Elsevier, vol. 261(C).
    8. Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2019. "Time frequency relationship between energy consumption, economic growth and environmental degradation in the United States: Evidence from transportation sector," Energy, Elsevier, vol. 173(C), pages 706-720.
    9. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    2. Hossain, Mohammad Razib & Singh, Sanjeet & Sharma, Gagan Deep & Apostu, Simona-Andreea & Bansal, Pooja, 2023. "Overcoming the shock of energy depletion for energy policy? Tracing the missing link between energy depletion, renewable energy development and decarbonization in the USA," Energy Policy, Elsevier, vol. 174(C).
    3. Duan, Tianyao & Guo, Huan & Qi, Xiao & Sun, Ming & Forrest, Jeffrey, 2024. "A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China," Energy, Elsevier, vol. 304(C).
    4. Nusrat Farzana & Md Qamruzzaman & Yeasmin Islam & Piana Monsur Mindia, 2023. "Nexus between Personal Remittances, Financial Deepening, Urbanization, and Renewable Energy Consumption in Selected Southeast Asian Countries: Evidence from Linear and Nonlinear Assessment," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 270-287, November.
    5. Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
    6. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    7. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," Working Papers halshs-00564897, HAL.
    8. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    9. Growitsch Christian & Nepal Rabindra & Stronzik Marcus, 2015. "Price Convergence and Information Efficiency in German Natural Gas Markets," German Economic Review, De Gruyter, vol. 16(1), pages 87-103, February.
    10. Lee, Chi-Chuan & Lee, Chien-Chiang & Ning, Shao-Lin, 2017. "Dynamic relationship of oil price shocks and country risks," Energy Economics, Elsevier, vol. 66(C), pages 571-581.
    11. Nautz, Dieter & Strohsal, Till & Netšunajev, Aleksei, 2019. "The Anchoring Of Inflation Expectations In The Short And In The Long Run," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1959-1977, July.
    12. Antonia López Villavicencio & Josep Lluís Raymond Bara, 2006. "The short and long-run determinants of the real exchange rate in Mexico," Working Papers wpdea0606, Department of Applied Economics at Universitat Autonoma of Barcelona.
    13. Raphaël Chiappini & Dominique Torre & Elise Tosi, 2019. "Romania's Unsustainable Stabilization: 1929-1933," GREDEG Working Papers 2019-43, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    14. Guili Liao & Qimeng Liu & Rongmao Zhang & Shifang Zhang, 2022. "Rank test of unit‐root hypothesis with AR‐GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 695-719, September.
    15. Saaed, A.A.J., 2007. "Inflation and Economic Growth in Kuwait: 1985-2005. Evidence from Co-Integration and Error Correction Model," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(1).
    16. Demiralay, Sercan & Ulusoy, Veysel, 2014. "Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models," MPRA Paper 53229, University Library of Munich, Germany.
    17. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
    18. John Barkoulas & Christopher Baum & Mustafa Caglayan, 1999. "Fractional monetary dynamics," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1393-1400.
    19. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2017. "Do oil price asymmetric effects on the stock market persist in multiple time horizons?," Applied Energy, Elsevier, vol. 185(P2), pages 1799-1808.
    20. Bahmani-Oskooee, Mohsen & Bohl, Martin T., 2000. "German monetary unification and the stability of the German M3 money demand function," Economics Letters, Elsevier, vol. 66(2), pages 203-208, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1586-:d:1591431. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.