Abstract
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.
Highlights
China’s biofuel industry has developed rapidly in recent years
Some scholars have studied the grey prediction model combined with an algorithm such as the genetic algorithm [36], Verhulst power allocation [37], technique for order of preference by similarity to ideal solution (TOPSIS) [38], and particle swarm optimization (PSO) [39]
To tackle the influence of random fluctuation data on forecasting accuracy in the Markov chain model and the limitation of weak anti-interference ability, we introduce the fuzzy set theory and improve the traditional grey–Markov model using classifications of states
Summary
China’s biofuel industry has developed rapidly in recent years. On the one hand, China’s oil consumption is growing very fast, and China has overtaken the U.S as the world’s largest oil importer [1]. Obama promised to reduce the U.S.’ carbon emissions by 26% to 28% by 2025 [10] To achieve this goal, the global demand for biofuels in road transport is expected to rise from 32.4 billion gallons in to 51.1 billion gallons in 2022, a growth of nearly 60%. According to the long-term development prospects of renewable energy, by 2020, biofuels could account for 15% of transportation energy consumption, with biofuels substituting 10% of conventional fuels [12] Keeping these factors in mind and given the rise in China’s overall biofuel production from 2003 to 2012, it is important to devise accurate methods to see whether the production can achieve the long-term development of renewable energy
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