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

Energies

Nana Geng²  Yong Zhang²  Yixiang Sun¹  Yunjian Jiang²  Dandan Chen² 

[1] School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China; E-Mail:;School of Transportation, Southeast University, Nanjing 210096, Jiangsu, China; E-Mails:

关键词: grey model;    Markov model;    fuzzy set theory;    combination forecast;    biofuel production;   

DOI  :  10.3390/en81012080

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.