Carbon dioxide emission prediction using support vector machine

2nd International Manufacturing Engineering Conference; 3rd Asia-Pacific Conference on Manufacturing Systems

Saleh, Chairul^1 ; Dzakiyullah, Nur Rachman^2 ; Nugroho, Jonathan Bayu^1

Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Islam Indonesia, Yogyakarta, Indonesia^1

Faculty of Information Technology and Communication, Universiti Teknikal Malaysia Melaka (UTEM), Melaka, Malaysia^2

关键词: Business operation;    Carbon dioxide emissions;    Cross-validation technique;    Error of the models;    Production process;    Root mean square errors;    Training and testing;    Trial-and-error approach;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/114/1/012148/pdf
DOI  :  10.1088/1757-899X/114/1/012148

来源: IOP

【 摘 要 】

In this paper, the SVM model was proposed for predict expenditure of carbon (CO2) emission. The energy consumption such as electrical energy and burning coal is input variable that affect directly increasing of CO2 emissions were conducted to built the model. Our objective is to monitor the CO2 emission based on the electrical energy and burning coal used from the production process. The data electrical energy and burning coal used were obtained from Alcohol Industry in order to training and testing the models. It divided by cross-validation technique into 90% of training data and 10% of testing data. To find the optimal parameters of SVM model was used the trial and error approach on the experiment by adjusting C parameters and Epsilon. The result shows that the SVM model has an optimal parameter on C parameters 0.1 and 0 Epsilon. To measure the error of the model by using Root Mean Square Error (RMSE) with error value as 0.004. The smallest error of the model represents more accurately prediction. As a practice, this paper was contributing for an executive manager in making the effective decision for the business operation were monitoring expenditure of CO2 emission.