Forecasting Premium Motor Spirit (PMS) and Energy Commodities Prices Using Machine Learning Techniques: A Review
DOI:
https://doi.org/10.56919/usci.1122.025Keywords:
Premium Motor Spirit (PMS), Energy Commodities, Forecasting, Machine learningAbstract
The instability of Premium Motor Spirit (PMS/Petrol) and other energy commodities prices, occasioned by volatile and dynamic movement of prices has been found to affect the cost of production in Nigeria. As a result, this paper studies current literature on the applications of machine learning techniques in forecasting PMS (Petrol) and other energy commodities prices. The review has been done through an electronic search of the published papers in the last 4 years (2019-2022). A total number of twenty-nine (29) publications on PMS (Petrol) and other energy commodities prices forecasting using machine learning models were selected for the study to identify research gaps and future works. This paper covers a summary of reviewed published papers on forecasting PMS (Petrol) and other energy commodities prices using machine learning techniques, semantic analysis of algorithms used, and the taxonomy of the models adopted in the published papers. The results showed that there are studies that presented the application of machine learning models in forecasting the prices of PMS (Petrol) and other energy commodities in other countries. However, very few of them have proposed the construction of machine learning models for forecasting PMS (Petrol) and other energy commodities prices in Nigeria. This leads to the need to develop new models, especially deep learning hybrid models.
References
Aji, A.B., and Surjandari, I. (2020). Hybrid vector autoregression-recurrent neural networks to forecast multivariate time series jet fuel transaction price. IOP Conf. Series: Materials Science and Engineering, 909, 012079. [Crossref] https://doi.org/10.1088/1757-899X/909/1/012079
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. [Crossref] https://doi.org/10.1016/j.petrol.2020.107528
Berrisch, J., and Ziel, F. (2021). Distributional Modeling and Forecasting of Natural Gas Prices. 1-26. [Crossref]
Bitirgen, K., and Filik, Ü.B. (2020). Electricity Price Forecasting based on XGBooST and ARIMA Algorithms. Journal of Engineering Research and Technology, 1(1), 1-13.
Bouri, E., Dutta, A., and Saeed, T. (2020). Modeling and Analysis: Forecasting ethanol price volatility under structural breaks. Biofuels, Bioproducts & Biorefining, 1-7. [Crossref] https://doi.org/10.1002/bbb.2158
Bui-Duy, L., and Vu-Thi-Minh, N. (2021). Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia. Asian Journal of Shipping and Logistics, 37(1), 1-11. [Crossref] https://doi.org/10.1016/j.ajsl.2020.04.003
Escribano, Á., and Wang, D. (2020). Forecasting gasoline prices with mixed random forest error correction models. Economics, 1-32.
Fangping, Y., Yanqing, L., and Chenxi, Z. (2021). Forecasting the Price of Fuel Oil : A STL- (ELM + ARIMA) Combination Approach Forecasting the Price of Fuel Oil : A STL-(ELM + ARIMA) Combination Approach. [Crossref]
García Jolly, R., and Martín del Campo, C. (2021). A practical model for the supply chain growth optimization for automotive fuels in Mexico. Canadian Journal of Chemical Engineering, 99(4), 898-914. [Crossref] https://doi.org/10.1002/cjce.23890
Gunarto, T., Azhar, R., Tresiana, N., and Ahadiat, A. (2020). Accurate Estimated Model of Volatility Crude Oil Price. 10(5), 228-233. https://doi.org/10.32479/ijeep.9513
Gupta, N., and Nigam, S. (2020). Crude Oil Price Prediction using Artificial Neural Network. Procedia Computer Science, 170, 642-647. [Crossref] https://doi.org/10.1016/j.procs.2020.03.136
Herlina, M. (2020). Panel cointegration analysis in determining relationship of agricultural commodity and oil fuel price in Indonesia. Indonesian Journal of Statistics and Its Applications, 4(2), 341-358. https://doi.org/10.29244/ijsa.v4i2.662
Huang, Y., and He, Z. (2020). Science of the Total Environment Carbon price forecasting with optimization prediction method based on unstructured combination. Science of the Total Environment, 725, 138350. [Crossref] https://doi.org/10.1016/j.scitotenv.2020.138350
Khan, U., Yamamoto, T., and Sato, H. (2020). Consumer preferences for hydrogen fuel cell vehicles in Japan. Transportation Research Part D: Transport and Environment, 87, 102542. [Crossref] https://doi.org/10.1016/j.trd.2020.102542
Li, J., Wu, Q., Tian, Y., and Fan, L. (2021). Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network. Energy, 227, 120478. [Crossref] https://doi.org/10.1016/j.energy.2021.120478
Mardiana, S., Saragih, F., and Huseini, M. (2020). Forecasting gasoline demand in Indonesia using time series. International Journal of Energy Economics and Policy, 10(6), 132-145. [Crossref] https://doi.org/10.32479/ijeep.9982
Moon, J., and Jung, T.Y. (2020). A critical review of Korea's long-term contract for renewable energy auctions: The relationship between the import price of liquefied natural gas and system marginal price. Utilities Policy, 67(October), 101132. [Crossref] https://doi.org/10.1016/j.jup.2020.101132
Moshkbar-Bakhshayesh, K. (2020). Performance study of bayesian regularization based multilayer feed-forward neural network for estimation of the uranium price in comparison with the different supervised learning algorithms. Progress in Nuclear Energy, 127, 103439. [Crossref] https://doi.org/10.1016/j.pnucene.2020.103439
Nam, K.J., Hwangbo, S., and Yoo, C.K. (2020). A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea. Renewable and Sustainable Energy Reviews, 122(December 2019), 109725. [Crossref] https://doi.org/10.1016/j.rser.2020.109725
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. [Crossref] https://doi.org/10.36478/jeasci.2020.602.606
Panapakidis, I., Sourtzi, V. and Dagoumas, A. (2020). Forecasting the fuel consumption of passenger ships with a combination of shallow and deep learning. Electronics, 9(5): 776. [Crossref] https://doi.org/10.3390/electronics9050776
Perry, M. (2019). Predicting Retail Petrol Prices in Australian Restorative Markets (Time Series Forecasting). Deep Learning, Spring 2019, Stanford University, CA. 1-6.
Ricardianto, P., Agushinta, L., Sari, M., and Ikhsan, R.B. (2020). The strategy of the Indonesian aviation industry to face fluctuate exchange rate and the increase of aviation fuel price. Journal of Xi'an University of Architecture & Technology, 12(4), 4538-4546.
Shambulingappa, H.S. (2020). Crude oil price forecasting using machine learning. International Journal of Advanced Scientific Innovation, 1(1), 1-11. [Crossref]
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. [Crossref] https://doi.org/10.1016/j.eneco.2021.105170
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), 3305-3316. [Crossref] https://doi.org/10.15244/pjoes/114261
van Bentem, K.J.P. (2020). Techno-economic Analysis of Sustainable Aviation Fuels by Using Traffic Forecasts and Fuel Price Projections: a Case Study at TUI Aviation. M.Sc. Thesis. Department of Transport, Infrastructure & Logistics, Delft University of Technology Delft, the Netherlands.
Westarp, A.G von. (2020). A new model for the calculation of the bunker fuel speed-consumption relation. Ocean Engineering, 204, 107262. [Crossref] https://doi.org/10.1016/j.oceaneng.2020.107262
Yang, S., Chen, D., Li, S., and Wang, W. (2020). Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm. Science of the Total Environment, 716, 137117. [Crossref] https://doi.org/10.1016/j.scitotenv.2020.137117
Zhao, L.T., Miao, J., Qu, S., and Chen, X.H. (2021). A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction. Science of the Total Environment, 796, 149110. [Crossref] https://doi.org/10.1016/j.scitotenv.2021.149110
Żymełka, P., and Szega, M. (2021). Short-term scheduling of gas-fired CHP plant with thermal storage using optimization algorithm and forecasting models. Energy Conversion and Management, 231. [Crossref] https://doi.org/10.1016/j.enconman.2021.113860
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