Forecasting China’s Annual Biofuel Production Using an Improved Grey Model

• Published in: Energies (Basel) Vol. 8; no. 10; pp. 12080 - 12099
• Main Authors: Geng, Nana, Zhang, Yong, Sun, Yixiang, Jiang, Yunjian, Chen, Dandan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2015
• Subjects: Accuracy; Air pollution; Biodiesel fuels; biofuel production; Biofuels; Biomass; Carbon dioxide; combination forecast; Dynamics; Economic forecasting; Emissions; Energy consumption; Energy industry; Forecasting; Fuels; Fuzzy; Fuzzy set theory; Fuzzy sets; grey model; Markov model; Mathematical models; Oil consumption; Outdoor air quality; Policies; Renewable resources; Set theory; System theory; United States > US; China; China, People's Rep
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en81012080

Biofuel production in China suffers from many uncertainties due to concerns about the government's support policy and supply of biofuel raw material. Predicting biofuel production is critical to the development of this energy industry. Depending on the biofuel's characteristics, we improve the prediction precision of the conventional prediction method by creating a dynamic fuzzy grey-Markov prediction model. Our model divides random time series decomposition into a change trend sequence and a fluctuation sequence. It comprises two improvements. We overcome the problem of considering the status of future time from a static angle in the traditional grey model by using the grey equal dimension new information and equal dimension increasing models to create a dynamic grey prediction model. To resolve the influence of random fluctuation data and weak anti-interference ability in the Markov chain model, we improve the traditional grey-Markov model with classification of states using the fuzzy set theory. Finally, we use real data to test the dynamic fuzzy prediction model. The results prove that the model can effectively improve the accuracy of forecast data and can be applied to predict biofuel production. However, there are still some defects in our model. The modeling approach used here predicts biofuel production levels based upon past production levels dictated by economics, governmental policies, and technological developments but none of which can be forecast accurately based upon past events.