2012 ©
Journal Publication
Research Title A Comparison of Forecasting Models using Multiple Regression and Artificial Neural Networks for the Supply and Demand of Thai Ethanol 
Date of Distribution 12 December 2013 
     Title of the Conference The IEEE International Conference on Industrial Engineering and Engineering Management 
     Organiser IEEM 2013 Secretariat Office Meeting Matters International 
     Conference Place Bangkok 
     Province/State Thailand 
     Conference Date 10 December 2013 
     To 13 December 2013 
Proceeding Paper
     Volume 2013 
     Issue CFP13IEI-USB 
     Page ISBN 978-1-4799-0985-8 
     Editors/edition/publisher Rojanee Homchalee, Weerapat Sessomboon 
     Abstract This paper presented three types of models for forecasting the supply and demand of Thai ethanol, so called MR, ANN, and MR-ANN models. MR models were formulated using stepwise multiple regression analysis, which were statistically significant. However, MR models provided low performance in forecasting. ANN models were constructed using artificial neural networks, which provided satisfactory results. Moreover, the third type of models was an integration of multiple regression analysis and artificial neural networks. In MR-ANN models, influential factors from stepwise multiple regression, were taken as inputs for artificial neural networks. The integrated models provided a fair results comparing to the first two types of models. In summary, ANN models provided the lowest MAPE and the highest R2 indicating that the models were the most appropriate among the three types of models. ANN models are therefore recommended to forecast the supply and demand of Thai ethanol.  
527040013-3 Mrs. ROJANEE HOMCHALEE [Main Author]
Engineering Doctoral Degree

Peer Review Status มีผู้ประเมินอิสระ 
Level of Conference นานาชาติ 
Type of Proceeding Full paper 
Type of Presentation Oral 
Part of thesis true 
Presentation awarding false 
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