Forecasting the transportation energy demand with the help of optimization artificial neural network using an improved red fox optimizer (IRFO)

Yijie Liu; Gongxing Yan; Andrea Settanni

doi:10.1016/j.heliyon.2023.e21599

Forecasting the transportation energy demand with the help of optimization artificial neural network using an improved red fox optimizer (IRFO)

Heliyon. 2023 Nov 2;9(11):e21599. doi: 10.1016/j.heliyon.2023.e21599. eCollection 2023 Nov.

Authors

Yijie Liu¹, Gongxing Yan^{2

3}, Andrea Settanni^{4

5}

Affiliations

¹ Chongqing Creation Vocational College, Yongchuan 402160, Chongqing, China.
² School of Intelligent Construction, Luzhou Vocational and Technical College, Luzhou 646000, China.
³ Luzhou Key Laboratory of Intelligent Construction and Low-carbon Technology, Luzhou 646000, China.
⁴ University of Tirana, Tirana, Albania.
⁵ College of Technical Engineering, The Islamic University, Najaf, Iraq.

Abstract

Transportation energy demand has a significant impact on worldwide energy consumption and greenhouse gas emissions. Accurate transportation energy demand predictions can help policymakers develop and implement successful energy policies and strategies. In this study, a novel approach to predict transportation energy demand using the Artificial Neural Network (ANN) based on the Improved Red Fox Optimizer (IRFO) has been suggested. The proposed method utilizes the ANN model to solve the complex nonlinear relationships between transportation energy demand and its effective parameters including Gross Domestic Product (GDP), population, and vehicle numbers. Also, the IRFO algorithm was utilized to modify the ANN model's parameters to improve the prediction accuracy. The experimental findings demonstrate the ANN-IRFO model performs better than the other method in terms of accuracy and effectiveness. It predicts the growth of GDP, population, and vehicles number by 5.5 %, 4.8 %, and 4.2 %, respectively. The findings demonstrate that the suggested method can provide accurate forecasts for transportation energy demand, which can help decision-makers to make informed decisions and policies regarding energy management and sustainability.

Keywords: Energy demand; GDP; Optimization; Optimize ANN; Transportation.