Abstract—Refined oil, including gasoline, diesel, et al., is an
important fuel for transportation and other industries. With the
promotion of new energy, the demand for refined oil market
has formed a competitive relationship with the alternative
energies’ market. It is necessary to design and transform the
refined oil supply chain to meet market requirements and
ensure the balance of supply and demand. Forecasting the
demand of refined oil market is the important basis for
designing and transforming the refined oil supply chain.
Because BP neural network shows strong adaptability when
solving multi-parameter nonlinear problems, this paper
proposed a BP neural network model from the analysis of
conventional influence factors and special impact factors such
as the share of alternative energies’ market. The actual data
was tested to prove that the model could reflect the relationship
between the market share of alternative energy and the market
demand of refined oil. Analysis was given about the future
development of the refined oil market and alternative energy
based on the experimental results.
Index Terms—Demand forecasting, refined oil, alternative
energies competition, BP neural network.
Wan Zhang is with Instituto Superior Técnico, University of Lisbon,
Portugal and the National Engineering Laboratory for Pipeline
Safety/Beijing Key Laboratory of Urban oil and Gas Distribution
Technology, China University of Petroleum-Beijing, P. R. China (e-mail:
zhang.wan@tecnico.ulisboa.pt).
Yongtu Liang is with National Engineering Laboratory for Pipeline
Safety/Beijing Key Laboratory of Urban oil and Gas Distribution
Technology, China University of Petroleum-Beijing, Beijing 102249, P.R.
China (e-mail: liangyt21st@163.com).
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Cite:Wan Zhang and Yongtu Liang, "Regional Demand Forecasting of Refined Oil under Alternative Energy Market’s Competition," Journal of Clean Energy Technologies vol. 7, no. 4, pp. 56-59, 2019.
