Forecasting on first generation bioethanol production: A comparison of selected linear and non-linear models

Abstract

Sustainable bioethanol production depends on the continuity of feedstock supply. Thus, an appropriate tool for feedstock supply forecasting is necessary for bioethanol production. This study was aimed at using forecasting models to predict feedstock supply and then estimating how much bioethanol could be produced from the forecasted bioethanol feedstocks as a major investigation in Turkey. It focused on running the linear model as Auto-Regressive (AR) and non-linear models as Auto-Regressive eXogeneous (ARX) and Auto-Regressive Moving Average eXogeneous (ARMAX) to forecast annual potential of wheat, corn, barley, and sugar beet which could be used as feedstock to produce first generation bioethanol. First, model order determination and modeling of feedstock production were studied for long prediction horizons. Model orders were estimated with major model order selection criteria: Akaike Information Criterion (AIC) and Final Prediction Error in the AR model. The same model orders were also used for the ARX model to compare, whereas model orders were determined based on the performance of the ARMAX model. Second, model performances were tested using Root Mean Square, R2, Chi-Square (χ2), and AIC for optimum model orders of each series. Finally, feedstock forecasts were determined to be quantitatively consistent for each model and with legal authority predictions. There were negligible small differences ranging from 0.8% to 2%. It is concluded that barley and wheat supplies have significant potential to produce bioethanol except for their primary use in food, seed, and feed consumption. However, sugar beet and corn are mainly used in Turkey.