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Forecasting High Speed Diesel Demand in India with Econometric and Machine Learning Methods

Author

Listed:
  • Aarti Mehta Sharma

    (Symbiosis Centre for Management Studies, Bengaluru, Symbiosis International (Deemed University), Pune, Maharashtra, India)

  • Saina Baby

    (Symbiosis Institute of Business Management Bengaluru, Symbiosis International (Deemed University), Pune, Maharashtra, India,)

  • Varsha Raghu

    (Freelance Researcher, India.)

Abstract

According to International Energy Agency (IEA), India is expected to surpass China by 2024 to become the second largest consumer of oil in the world followed by the United States. High-Speed Diesel (HSD) has the biggest share in the total petroleum products consumed in India accounting for around 38% of the total consumption. Considering the volatile global oil market and an oil import dependency ratio of more than 80% during the last four years, the probability of supply disruptions is high in the Indian context. As any uncertainty about the supply of diesel can affect the smooth functioning of the economy and may create inflationary pressures. Accurate forecasting of HSD demand will be essential for appropriate supply management arrangements. Artificial Neural Networks (ANN) with Multi-Layer Perceptron (MLP) and extreme learning machines is used for forecasting diesel demand in this study. Demand forecasting has been carried out using monthly HSD demand data drawn from the “Indiastat†database for the period 1991-2022. Comparison of ANN with traditional forecasting methods of Autoregressive Integrated Moving Average(ARIMA)and Exponential Smoothing has also been undertaken in this study. This study identifies the deep learning technique of ANN with MLP as the best diesel demand forecasting technique.

Suggested Citation

  • Aarti Mehta Sharma & Saina Baby & Varsha Raghu, 2024. "Forecasting High Speed Diesel Demand in India with Econometric and Machine Learning Methods," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 496-506, January.
    • Handle: RePEc:eco:journ2:2024-01-54
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    References listed on IDEAS

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    1. Hasanov, Mübariz, 2015. "The demand for transport fuels in Turkey," Energy Economics, Elsevier, vol. 51(C), pages 125-134.
    2. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    3. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    4. Marbuah, George, 2014. "Understanding crude oil import demand behaviour in Ghana," MPRA Paper 60436, University Library of Munich, Germany.
    5. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    6. Huang, Menghao & Shao, Wei & Wang, Jian, 2023. "Correlations between the crude oil market and capital markets under the Russia–Ukraine conflict: A perspective of crude oil importing and exporting countries," Resources Policy, Elsevier, vol. 80(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    High Speed Diesel; Forecasting; Time Series; ANN; Accuracy; ARIMA; Exponential Smoothing;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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