Resources Science ›› 2017, Vol. 39 ›› Issue (6): 1048-1058.doi: 10.18402/resci.2017.06.06

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Early classification warning for regional energy security exogenous sources based on FI-GA-NN Model

Jian HU(), Jinhua SUN   

  1. School of Management,Chongqing University of Technology,Chongqing 400054,China
  • Received:2016-09-12 Revised:2017-03-06 Online:2017-06-20 Published:2017-06-20

Abstract:

In order to solve the problem of frequent occurrence of energy security incidents in China,we took the regional energy security exogenous source as focal research to develop effective early warning. We defined the concept of regional energy security exogenous source. From an external point of view we divided the root cause of regional energy events into three kinds of exogenous sources using analysis of typical regional energy security incidents,including energy price fluctuations,energy policy intervention and external environmental changes. Then,the attribute set and data set of regional energy security exogenous sources are constructed by extracting different cases. At the premise of considering exogenous source characteristics,such as suddenness nonlinear and complex,we designed a classification early warning model,known as the FI-GA-NN model,an exogenous source warning model with fuzzy integral (FI),genetic algorithm (GA)and neural network (NN). In this model,we have first of all designed the fuzzy integral method to make an exploration of the sample grade of exogenous source. Secondly,we adopted BP neural network methods as the early warning method. At the same time,a genetic algorithm was used to optimize the weights of BP neural network to improve the accuracy of the model. To verify the validity of the FI-GA-NN model,we have selected 30 typical cases of energy security exogenous source to make the classification early warning report during the years from 1999 to 2015. The training results showed that the FI-GA-NN model had fast convergence speed. The model error was less than the expected value when the model was trained to the 717th step. After approximately 60 generations of searching, the actual output value of the model training was closer to the expected output value. The testing results show that the classification warning accuracy rate of FI- GA- NN was higher. The classification early warning results of the regional energy security exogenous source prove to be well in agreement with actual conditions. Therefore, it is reasonable and effective to apply the FI- GA- NN model to the monitoring the regional energy security situations.

Key words: regional energy security, exogenous source, classification early warning, fuzzy integral, genetic algorithm, neural network