Modeling of Highway Energy Consumption by Artificial Intelligence and Regression Methods

摘要

The relationship between work and energy,which is based on nature,shows us that energy is the condition for action and ability for doing work.While developing technology and industrialization factors increase production,it also causes an increase in energy consumption.The transportation sector,which is a branch of industrialization,has an important place on the basis of sector in energy consumption.In this study,energy consumption are studied in transportation sector especially road transportation of freight is high potential in Turkey.Within the scope of the study,energy consumption prediction modeling is made by using artificial neural networks (ANN) and adaptive neural fuzzy inference system (ANFIS) from artificial intelligence techniques,and multivariate linear regression (MLR) methods from regression techniques.In modeling,highway road network length,vehicle-km,weighted average daily traffic (WADT),number of motor vehicles and population parameters are examined as independent variables.When comparing the prediction models,the determination coefficient (R2),the mean square error (MSE) and the average percentage error (APE) performance criteria are taken into consideration.According to performance criteria,the best model is obtained by linear regression method.R2,HKO,OYH values of the best model are 0.9474,54084 and 4.86%,respectively.With the developed model,it is aimed to direct transportation policies.