Transportation energy demand forecasting in Taiwan based on metaheuristic algorithms

Ali Lashgari, Hasan Hosseinzadeh, Mohammad Khalilzadeh, Bahar Milani, Amin Ahmadisharaf, Shima Rashidi

Research output: Contribution to journalArticlepeer-review


A new methodology is suggested in this study to provide optimum forecasting of the future transportation energy demand in Taiwan. The paper introduces a new improved version of Emperor Penguin Optimizer (IEPO) to provide an optimal and suitable forecasting model. The forecasting was based on three different models including linear, exponential, and quadratic where their coefficients have been optimized using the suggested IEPO algorithm which is based on considering the population, the GDP growth rate, and the total annual vehicle-km. The study considers two different scenarios based on curve fitting and projection data. The results indicate that the RMS value for the TED forecasting based on the proposed IEPO algorithm applied to the linear, exponential, and Quadratic for training are 0.0452, 0.0461, and 0.0492, respectively and for testing are 0.0456, 0.0596, and 0.0642, respectively. This shows better results of the optimized exponential method’s efficiency. Simulation results showed high efficiency for the proposed IEPO-based transportation energy demand forecasting based on all of the employed models for decision-making in ROC. © 2022 Taylor & Francis Group, LLC.
Original languageSpanish
Pages (from-to)2782-2800
Number of pages19
JournalEnergy Sources, Part A: Recovery, Utilization, and Environmental Effects
StatePublished - 12 Apr 2022

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