نهمین کنفرانس بین المللی فناوری و مدیریت انرژی

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کد مقاله icemt9-1059

عنوان Advanced Predictive Modeling of Pollutant Gas Emissions in the Automotive Industry based on Machine Learning

نویسندگان Ashkan Safari - Hamed Kheirandish Gharehbagh - Morteza Nazari-Heris - Omid Halimi Milani - Hamed Kharrati - Afshin Rahimi

چکیده Predicting CO2 emissions in the automotive industry is vital for driving innovation in fuel efficiency, shaping policies, and fostering a greener, sustainable future. An advanced predictive modeling approach for estimating CO2 emissions in the automotive industry using machine learning techniques is presented in this paper. Data from 46 distinct automotive brands was incorporated, comprehensively analyzing various vehicles. The predictive model employed six numeric features, encompassing engine size, cylinder count, and diverse fuel consumption metrics, along with five categorical features concerning brand, model, vehicle class, transmission, and fuel type. Considerable results were achieved, with a mean squared error (MSE) of 29.99, a root mean squared error (RMSE) of 5.48, and an R2 of 0.991, showcasing the model's forecasting accuracy for CO2 emissions. Therefore, this work underscores the effectiveness of machine learning in CO2 emissions prediction and emphasizes the importance of considering diverse features and multiple automotive brands for constructing comprehensive and robust models in the context of environmental impact assessment, thereby contributing to a more sustainable automotive industry.

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