基于FI-GA-NN融合的区域能源安全外生警源分级预警研究
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摘要
区域能源安全预警研究对于解决中国现阶段区域能源安全突发事件频现问题,保障区域经济与区域安全协调发展具有重要现实意义。本文以区域能源安全外生警源为研究对象,通过对区域能源安全事件案例收集及整理,构建了能源安全外生警源预警指标和数据集。融合模糊积分(Fuzzy Integral)、遗传算法(Genetic Algorithm)和神经网络(Neural Network)等方法的基本原理,设计了区域能源安全外生警源分级预警的FI-GA-NN模型,该模型首先利用模糊积分方法评估出区域能源安全外生警源样本分级预警的期望值,然后利用训练样本对遗传神经网络进行训练,最后对外生警源测试样本进行分级预警。实验测试结果表明:(1)利用FI-GA-NN模型对外生警源训练样本(1999-2006年)进行拟合训练,模型收敛速度快,训练到第717步时,模型误差平方和小于期望值。经过大约60代的搜索后模型的拟合度趋于稳定,模型训练的实际输出值与期望输出值较接近;(2)利用FI-GA-NN模型对能源安全外生警源测试样本(2007-2015年)进行分级预警,预警准确率较高,能有效提高区域能源安全外生警源预警的正确性,降低预警风险,模型体现出了较强的应用价值。
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 from1999 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 717 th 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.
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 from1999 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 717 th 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.
引文
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[15]徐龙君,吴江,李洪强.重庆开县井喷事故的环境影响分析[J].中国安全科学学报,2005,15(5):84-87.[Xu L J,Wu J,Li H Q.Analysis on environment impact of Chongqing Kaixian blowout[J].China Safety Science Journal,2005,15(5):84-87.]
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[20]李铁,金世军,鲁顺,等.辽宁电网“3.4”事故处理过程及分析[J].电网技术,2007,31(11):38-41.[Lie T,Jin S J,Lu S,et al.Treatment process and analysis of blackout occurred in Liaoning power grid on March 4,2007[J].Power System Technology,2007,31(11):38-41.]
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[22]刘晓燕,吕涛.突发性能源短缺应急主体演化博弈研究[J].中国人口·资源与环境,2016,26(5):154-159.[Liu X Y,Lv T.Evolutionary game theory study on sudden energy emergency[J].China Population,Resources and Environment,2016,26(5):154-159.]
[23]刘如山,张美晶,邬玉斌,等.汶川地震四川电网震害及功能失效研究[J].应用基础与工程科学学报,2010,18(7):200-211.[Liu R S,Zhang M J,Wu Y B,et al.Damage and failure study of Sichuan electric power grid in Wenchuan earthquake[J].Journal of Basic Science and Engineering,2010,18(7):200-211.]
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