AN ENHANCED MODEL FOR PREMIUM MOTOR SPIRIT (PMS) PRICE PREDICTION AND MANAGEMENT IN NIGERIA USING MACHINE LEARNING

Authors

  • Kizito Eluemunor Anazia
    Delta State University of Science and Technology, Ozoro
  • Emmanuel Obiajulu Ojei
  • O. Jahswill Ekibade
  • Rebecca Okeoghene Idama

Keywords:

Premium Motor Spirit, Machine Learning, Price Prediction, Management

Abstract

Forecasting Premium Motor Spirit (PMS) prices accurately is crucial for economic stability and effective decision-making in Nigeria. Premium Motor Spirit (PMS), which is also known as petrol or fuel, plays a pivotal role in the country's economy, impacting transportation costs, inflation rates, and overall economic growth. However, the unpredictability of PMS prices prediction and management is influenced by factors like government policies, international oil markets, supply chain disruptions, stakeholders and interested cartels amongst others. This has created a constant price fluctuation, poor price control and management which eventually lead to fuel scarcity and high cost of fuel. The price control mechanism remains a contentious issue, with debates over its impact on the economy, government spending, and the welfare of ordinary Nigerians. This article presents an enhanced model for Premium Motor Spirit (PMS) price prediction and management in Nigeria using machine learning that will improve the price prediction and management system that will produce high degree of accuracy. The system was developed using visual studio C#, ML.Net model and Microsoft SQL server for its backend database. This model identifies key factors impacting PMS prices prediction and management that is used to forecast PMS prices over a specified time horizon such as daily, weekly, or monthly thereby enhancing economic planning and stability in Nigeria.

Dimensions

Abubakar, T., Mukhtar, A., Afolabi, Eyitayo, A. and Lamido, I. (2024). Machine Learning Prediction of Volume Fraction Of Gas-Hydrates in Natural Gas Pipelines in Offshore Niger Delta, Vol. 8 No. 3 FUDMA Journal of Sciences - Vol. 8 No. 3.

Agbo, S. O., Yemi-Peters Victoria Ifeoluwa, and Adewumi Sunday Eric. (2022). Forecasting Premium Motor Spirit (PMS) and Energy Commodities Prices Using Machine Learning Techniques: A Review. UMYU Scientifica, 1(1), 194203. https://doi.org/10.56919/usci.1122.025.

Adeniran, A. and Alabi, T. (2023). Machine Learning in Economic Forecasting: A Case Study of Nigerian PMS Prices. Journal of Economic Studies, 45(2), 189-203.

Al-Fattah, S.M. (2020). A New Artificial Intelligence GANNATS Model Predicts Gasoline Demand of Saudi Arabia. Journal of Petroleum Science and Engineering, 194(June), 107528.

Anazia, K. E., Ojei, E. and Okpor, M.D. (2024). A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm, International Journal of Scientific Research and Management (IJSRM) ||Volume||12||Issue||03||Pages||1081-1093||2024|| Website: https://ijsrm.net ISSN (e): 2321-3418 DOI: 10.18535/ijsrm/v12i03.ec06.

Anazia, K. E. and Okpako, E. O. (2022). An Analysis of Machine Learning Techniques for Verifying and Improving Veracity in Big Data, International Journal of Scientific Research and Engineering Development- Volume 5 Issue 3, May-June 2022 Available at www.ijsred.com

Babalola, K. and Salau, O. (2020) Impact of Fallen Oil Prices on the Nigeria Economy. Journal of Poverty, Investment and Development www.iiste.org SSN 2422-846X An International Peer-reviewed Journal Vol.33, 2017.

Bitirgen, K., and Filik, .B. (2020). Electricity Price Forecasting based on XGBooST and ARIMA Algorithms. Journal of Engineering Research and Technology, 1(1), 1-13.

Eze, M. and Nwokoye, I. (2022). Advanced Predictive Models for Energy Commodities: A Comparative Analysis. Energy Economics Review, 37(4), 455-472.

Garca Jolly, R. and Martn, del C, C. (2021). A practical model for the supply chain growth optimization for automotive fuels in Mexico. Canadian Journal of Chemical Engineering, 99(4), 898914.

Gupta, N., and Nigam, S. (2020). Crude Oil Price Prediction using Artificial Neural Network. Procedia Computer Science, 170, 642647

John, N., Weldon, K., Everlyne, N., Njambi, N. and Gikunda, K. (2023). Petroleum Prices Prediction Using Data Mining Techniques A Review. Journal of Computer Science, 5(1), 45-55.

Martin, J. and Thompson, R. (2024). Challenges and Benefits of Ethanol Blends in Petrol. Renewable Energy Review, 32(1), 56-69.

Nelson, J., Ekokeme, T. T., Peter, E. A. and Eze, G. (2018). Impact of Crude Oil Price Changes on Economic Growth in Nigeria. 6(5). https://www.Ijsrc/2347-9698.

Obi, C. and Adeoye, O. (2023). Data-Driven Decision Making in the Energy Sector: Opportunities and Challenges. International Journal of Energy Research, 41(1), 103-120.

Odido, O.A., Effiong, S. A. and Eneje, E.B. (2023). Effect of Crude Oil Price Fluctuations on the Growth Rate of Nigerian Economy (1997 - 2022).International Research Journal of Innovations in Engineering and Technology (IRJIET) ISSN (online): 2581-3048 Volume 7, Issue 9, pp 11-29.

Olayiwola, M.F., and Seeletse, S.M. (2020). Statistical forecasting of petrol price in South Africa. Journal of Engineering and Applied Sciences,15(2): 602-606.

Shambulingappa, H.S. (2020). Crude oil Price Forecasting Using Machine Learning. International Journal of Advanced Scientific Innovation, 1(1), 111

Smith, L.V., Tarui, N., and Yamagata, T. (2021). Assessing the Impact of COVID-19 on Global Fossil Fuel Consumption and CO2 Emissions. Energy Economics, 97, 105170.

Sultan, Z.A., Alkhateeb, T.T.Y., Fawaz, M. M. (2020). Empirical investigation of relationship

between oil price and inflation: The case of India, International Journal of Energy Economics and Policy, 10(3), 90- 94. https://doi.org/10.32479/ijeep.9015

Tayo, O. P. and Adepoju, A. A. (2022). Modelling liquIfied Petroleum Gas Prices in Nigeria using Machine Learning Models, International Journal of Data Science, 3(2), 93-100.

Sun, W., Sun, C., and Li, Z. (2020). A Hybrid Carbon Price Forecasting Model with External and Internal Influencing Factor Considered Comprehensively: A case Study From China. Polish JOURNAL of Environmental Studies, 29(5), 33053316

Usman, Y. and Adebayo, S. (2023). Enhancing Economic Resilience through AI: The Case of PMS Price Forecasting in Nigeria. African Journal of Economic Policy, 12(3), 299-315.

Yusha'u, E. and Nkiru, C. (2024). Machine Learning-Based Approach for Diagnosing Intestinal Parasitic Infections in Northern Nigeria, Vol. 8 No. 3: FUDMA Journal of Sciences - Vol. 8 No. 3 (Special Issue)

Published

31-12-2024

How to Cite

AN ENHANCED MODEL FOR PREMIUM MOTOR SPIRIT (PMS) PRICE PREDICTION AND MANAGEMENT IN NIGERIA USING MACHINE LEARNING. (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 271-279. https://doi.org/10.33003/fjs-2024-0806-2926

Issue

Vol. 8 No. 6 (2024): FUDMA Journal of Sciences - Vol. 8 No. 6 (Special Issue)

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

AN ENHANCED MODEL FOR PREMIUM MOTOR SPIRIT (PMS) PRICE PREDICTION AND MANAGEMENT IN NIGERIA USING MACHINE LEARNING. (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 271-279. https://doi.org/10.33003/fjs-2024-0806-2926